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@y{y19,y={2019}} @ARTICLE{ubi_sensors2019, author = {Attila Reiss and Philip Schmidt and Ina Indlekofer and Kristof Van Laerhoven}, title = {Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks}, journal = {Sensors}, volume = {19}, year = {2019}, number = {14}, articlenumber = {28}, journalurl = {https://www.mdpi.com/journal/sensors}, issn = {}, abstract = {PPG-based continuous heart rate monitoring is essential in a number of domains, e.g. for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation. However, existing approaches are highly parametrised and optimised for specific scenarios of small public datasets. We address this fragmentation by contributing research into the robustness and generalisation capabilities of PPG-based heart rate estimation approaches. First, we introduce a novel large-scale dataset (called PPG-DaLiA), including a wide range of activities performed under close to real-life conditions. Second, we extend a state-of-the-art algorithm, significantly improving its performance on several datasets. Third, we introduce deep learning to this domain, and investigate various convolutional neural network architectures. Our end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output. Finally, we compare the novel deep learning approach to classical methods, performing evaluation on four public datasets. We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31% on the new dataset PPG-DaLiA, and by 21% on the dataset WESAD.}, doi = {10.3390/s19143079} } @inproceedings{ubi_iswc2019, author = {Philip Schmidt and Robert Duerichen and Attila Reiss and Thomas Ploetz and Van Laerhoven, Kristof}, title = {Multi-target Affect Detection in the Wild: An Exploratory Study }, booktitle = {Proceedings of the 2019 {ACM} International Symposium on Wearable Computers, {ISWC} 2019, London, UK, September 9-13, 2019}, publisher = {{ACM}}, year = {2019}, abstract = {Affective computing aims to detect a person's affective state (e.g. emotion) based on observables. The link between affective states and biophysical data, collected in lab settings, has been established successfully. However, the number of realistic studies targeting affect detection in the wild is still limited. In this paper we present an exploratory field study, using physiological data of 11 healthy subjects. We aim to classify arousal, State-Trait Anxiety Inventory (STAI), stress, and valence self-reports, utilizing feature-based and CNN methods. In addition, we extend the CNNs to multi-task CNNs, classifying all labels of interest simultaneously. Comparing the F1 score averaged over the different tasks and classifiers the CNNs reach an 1.8% higher score than the classical methods. However, the F1 scores barely exceed 45%. In the light of these results, we discuss pitfalls and challenges for physiology-based affective computing in the wild.}, isbn = {978-1-4503-6870-4/19/09}, doi = {10.1145/3341163.3347741} } @inproceedings{ubi_eusipco2019, author = {Christian Reich and Christina Nicolaou and Ahmad Mansour and Kristof van Laerhoven}, title = {Bayesian Estimation of Recurrent Changepoints for Signal Segmentation and Anomaly Detection }, booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)}, publisher = {IEEE}, year = {2019}, abstract = {Signal segmentation is a generic task in many time series applications. We propose approaching it via Bayesian changepoint algorithms, i.e., by assigning segments between changepoints. When successive signals show a recurrent changepoint pattern, estimating changepoint recurrence is beneficial for two reasons: While recurrent changepoints yield more robust signal segment estimates, non-recurrent changepoints bear valuable information for unsupervised anomaly detection. This study introduces the changepoint recurrence distribution (CPRD) as an empirical estimate of the recurrent behavior of observed changepoints. Two generic methods for incorporating the estimated CPRD into the process of assessing recurrence of future changepoints are suggested. The knowledge of non-recurrent changepoints arising from one of these methods allows additional unsupervised anomaly detection. The quality both of changepoint recurrence estimation via CPRD and of changepoint-related signal segmentation and unsupervised anomaly detection are verified in a proof-of-concept study for two exemplary machine tool monitoring tasks.}, doi = {10.23919/EUSIPCO.2019.8902774} } @ARTICLE{ubi_sensors2019b, author = {Philip Schmidt and Attila Reiss and Robert Duerichen and Kristof Van Laerhoven}, title = {Wearable-Based Affect Recognition - A Review}, journal = {Sensors}, year = 2019, month = {sep}, volume = {19}, number = {19}, pages = {4079}, journalurl = {https://www.mdpi.com/journal/sensors}, issn = {}, abstract = {Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person’s decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems enable an ideal platform for long-term affect recognition applications due to their rich functionality and form factor, while providing valuable insights during everyday life through integrated sensors. However, existing literature surveys lack a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods and best practices of wearable affect and stress recognition. Following a summary of different psychological models, we detail the influence of affective states on the human physiology and the sensors commonly employed to measure physiological changes. Then, we outline lab protocols eliciting affective states and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain and review common approaches related to the preprocessing, feature extraction and classification steps. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field to conduct and evaluate user studies and develop wearable systems.}, doi = {10.3390/s19194079} } @article{ubi_mdpi_inf2019, author = {Reich, Christian and Mansour, Ahmad and Van Laerhoven, Kristof}, title = {Collecting Labels for Rare Anomalies via Direct Human Feedback - An Industrial Application Study}, journal = {Informatics}, volume = {6}, year = {2019}, number = {3}, number = {38}, journalurl = {https://www.mdpi.com/journal/informatics}, issn = {2227-9709}, abstract = {Many systems rely on the expertise from human operators, who have acquired their knowledge through practical experience over the course of many years. For the detection of anomalies in industrial settings, sensor units have been introduced to predict and classify such anomalous events, but these critically rely on annotated data for training. Lengthy data collection campaigns are needed, which tend to be combined with domain expert annotations of the data afterwards, resulting in costly and slow process. This work presents an alternative by studying live annotation of rare anomalous events in sensor streams in a real-world manufacturing setting by experienced human operators that can also observe the machinery itself. A prototype for visualization and in situ annotation of sensor signals is developed with embedded unsupervised anomaly detection algorithms to propose signals for annotation and which allows the operators to give feedback on the detection and classify anomalous events. This prototype allowed assembling a corpus of several weeks of sensor data measured in a real manufacturing surrounding and was annotated by domain experts as an evaluation basis for this study. The evaluation of live annotations reveals high user motivation after getting accustomed to the labeling prototype. After this initial period, clear anomalies with characteristic signal patterns are detected reliably in visualized envelope signals. More subtle signal deviations were less likely to be confirmed an anomaly due to either an insufficient visibility in envelope signals or the absence of characteristic signal patterns.}, doi = {10.3390/informatics6030038} } @book{ubi_hasca2019, title = {Human Activity Sensing: Corpus and Applications}, series = {Springer Series in Adaptive Environments}, year = {2019}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, doi={10.1007/978-3-030-13001-5}, pages={248}, address = {Heidelberg}, abstract = {Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.}, isbn = {978-3-030-13000-8}, author = {Kawaguchi, Nobuo and Nishio, Nobuhiko and Roggen, Daniel and Inoue, Sozo and Pirttikangas, Susanna and Van Laerhoven, Kristof}, doi = {10.1007/978-3-030-13001-5} } @incollection{ubi_hasca2019a, author={Philipp M. Scholl and Benjamin Voelker and Bernd Becker and Kristof Van Laerhoven}, editor={Kawaguchi, Nobuo and Nishio, Nobuhiko and Roggen, Daniel and Inoue, Sozo and Pirttikangas, Susanna and Van Laerhoven, Kristof}, title={A Multi-media Exchange Format for Time-Series Dataset Curation}, bookTitle={Human Activity Sensing: Corpus and Applications}, year={2019}, publisher={Springer International Publishing}, pages={111--119}, abstract={Exchanging data as character-separated values (CSV) is slow, cumbersome and error-prone. Especially for time-series data, which is common in Activity Recognition, synchronizing several independently recorded sensors is challenging. Adding second level evidence, like video recordings from multiple angles and time-coded annotations, further complicates the matter of curating such data. A possible alternative is to make use of standardized multi-media formats. Sensor data can be encoded in audio format, and time-coded information, like annotations, as subtitles. Video data can be added easily. All this media can be merged into a single container file, which makes the issue of synchronization explicit. The incurred performance overhead by this encoding is shown to be negligible and compression can be applied to optimize storage and transmission overhead.}, isbn={978-3-030-13001-5}, doi={10.1007/978-3-030-13001-5_8} } @incollection{ubi_hascab2019, doi = {10.1007/978-3-030-13001-5_2}, year = 2019, publisher = {Springer International Publishing}, pages = {17--28}, author = {Philipp M. Scholl and Kristof Van Laerhoven}, title = {Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data}, booktitle = {Human Activity Sensing: Corpus and Applications}, abstract = {Every benchmark dataset that contains inertial data (acceleration, rate-of-turn, magnetic flux) requires a thorough description of the datasets itself. This description tends often to be unstructured, and supplied to researchers as a conventional description, and in many cases crucial details are not available anymore. In this chapter, we argue that each sensor modality exhibits particular statistical properties that allow to reconstruct the modality solely from the sensor data itself. In order to investigate this, tri-axial inertial sensor data from five publicly available datasets are analysed. We found that in particular three statistical properties, the mode, the kurtosis, and the number of modes tend to be sufficient for classification of sensor modality - requiring as the only assumption that the sampling rate and sample format are known, and the fact that that acceleration and magnetometer data is present in the dataset. With those assumption in place, we found that 98% of all 1003 data points were successfully classified.} } @inproceedings{ubi_earcomp2019a, author = {Alexander Hölzemann and Henry Odoemelem and Kristof Van Laerhoven}, title = {Using an in-ear wearable to annotate activity data across multiple inertial sensors}, booktitle = {EarComp’19, September 9, 2019, London, United Kingdom}, year = {2019}, publisher = {ACM Press}, abstract = {Wearable activity recognition research needs benchmark data, which rely heavily on synchronizing and annotating the inertial sensor data, in order to validate the activity classifiers. Such validation studies become challenging when recording outside the lab, over longer stretches of time. This paper presents a method that uses an inconspicuous, ear-worn device that allows the wearer to annotate his or her activities as the recording takes place. Since the ear-worn device has integrated inertial sensors, we use cross-correlation over all wearable inertial signals to propagate the annotations over all sensor streams. In a feasibility study with 7 participants performing 6 different physical activities, we show that our algorithm is able to synchronize signals between sensors worn on the body using cross-correlation, typically within a second. A comfort rating scale study has shown that attachment is critical. Button presses can thus define markers in synchronized activity data, resulting in a fast, comfortable, and reliable annotation method.}, doi = {10.1145/3345615.3361136} } @inproceedings{ubi_iwoar2019, author = {Sebastian Boettcher and Nikolay V. Manyakov and Nino Epitashvili and Amos Folarin and Mark Richardson and Matthias Duempelmann and Andreas Schulze-Bonhage and Kristof Van Laerhoven}, title = {Using Multimodal Biosignal Data from Wearables to Detect Focal Motor Seizures in Individual Epilepsy Patients}, booktitle = {Proceedings of the 6th International Workshop on Sensor-based Activity Recognition and Interaction}, year = {2019}, publisher = {ACM Press}, abstract = {Epilepsy seizure detection with wearable devices is an emerging research field. As opposed to the gold standard that is currently practiced, consisting of simultaneous video and EEG monitoring of patients, wearables have the advantage that they put a lower burden on epilepsy patients. This paper reports on the first stages in a research effort that is dedicated to the development of a multimodal seizure detection system specifically for focal onset epileptic seizures. In a focused analysis on data from three in-hospital patients with each having six to nine seizures recorded, we show that this type of seizures can manifest very differently and thus significantly impact classification. Using a Random Forest model on a rich set of features, we have obtained overall precision and recall scores of up to 0.92 and 0.72 respectively. These results show that the approach has validity, but we identify the type of focal seizure to be a critical factor for the classification performance.}, doi = {10.1145/3361684.3361687} } @inproceedings{ubi_assets2019, author = {Soudi, Abdelhadi and Van Laerhoven, Kristof and Bou-Souf, Elmostafa}, title = {AfricaSign -- A Crowd-sourcing Platform for the Documentation of STEM Vocabulary in African Sign Languages}, booktitle = {The 21st International ACM SIGACCESS Conference on Computers and Accessibility}, series = {ASSETS '19}, year = {2019}, isbn = {978-1-4503-6676-2}, location = {Pittsburgh, PA, USA}, pages = {658--660}, numpages = {3}, url = {http://doi.acm.org/10.1145/3308561.3354592}, doi = {10.1145/3308561.3354592}, acmid = {3354592}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {africa, avatar technology, documentation, sign language, stem}, abstract = {Research in sign languages, in general, is still a relatively new topic of study when compared to research into spoken languages. Most of African sign languages are endangered and severely under-studied [11]. In an attempt to (lexically) document as many endangered sign languages in Africa as possible, we have developed a low-barrier, online crowd-sourcing platform (AfricaSign) that enables the African deaf communities to document their sign languages. AfricaSign offers to users multiple input modes, accommodates regional variation in multiple sign languages and allows the use of avatar technology to describe signs. It is likely that this research will uncover typological features exhibited by African sign languages. Documentation of STEM vocabulary will also help facilitate access to education for the Deaf community.} } @inproceedings{ubi_earcomp2019b, author = {Henry Odoemelem and Alexander Hölzemann and Kristof Van Laerhoven}, title = {Using the eSense Wearable Earbud as a Light-Weight Robot Arm Controller}, booktitle = {EarComp’19, September 9, 2019, London, United Kingdom}, year = {2019}, publisher = {ACM Press}, abstract = {Head motion-based interfaces for controlling robot arms in real time have been presented in both medical-oriented re- search as well as human-robot interaction. We present an especially minimal and low-cost solution that uses the eS- ense [1] ear-worn prototype as a small head-worn controller, enabling direct control of an inexpensive robot arm in the environment. We report on the hardware and software setup, as well as the experiment design and early results.}, doi = {10.1145/3345615.3361138} } @book{ubi_iot2020, doi = {10.1007/978-3-030-28925-6}, year = 2020, publisher = {Springer International Publishing}, author = {Rui José and Kristof Van Laerhoven and Helena Rodrigues}, title = {3rd {EAI} International Conference on {IoT} in Urban Space}, abstract = {We are delighted to introduce the proceedings of the third edition of the 2018 European Alliance for Innovation (EAI) International Conference on IoT in Urban Space(Urb-IoT), co-located with the Smart City 360◦ Summit 2018, which took place in Guimarães, Portugal. This conference has brought together researchers, developers, and practitioners around the world who are exploring the urban space and its dynamics within the scope of the Internet of Things (IoT) and the new science of cities.} } @inproceedings{ubi_dsd2019, doi = {10.1109/dsd.2019.00106}, url = {https://doi.org/10.1109%2Fdsd.2019.00106}, year = 2019, month = {aug}, publisher = {{IEEE}}, author = {Sebastian Vogel and Rajatha B. Raghunath and Andre Guntoro and Kristof Van Laerhoven and Gerd Ascheid}, title = {Bit-Shift-Based Accelerator for {CNNs} with Selectable Accuracy and Throughput}, booktitle = {2019 22nd Euromicro Conference on Digital System Design ({DSD})}, abstract = {Hardware accelerators for compute intensive algorithms such as convolutional neural networks benefit from number representations with reduced precision. In this paper, we evaluate and extend a number representation based on power-of-two quantization enabling bit-shift-based processing of multiplications. We found that weights of a neural network can either be represented by a single 4 bit power-of-two value or with two 4 bit values depending on accuracy requirements. We evaluate the classification accuracy of VGG-16 and ResNet50 on the ImageNet dataset with weights represented in our novel number format. To include a more complex task, we additionally evaluate the format on two networks for semantic segmentation. In addition, we design a novel processing element based on bit-shifts which is configurable in terms of throughput (4 bit mode) and accuracy (8 bit mode). We evaluate this processing element in an FPGA implementation of a dedicated accelerator for neural networks incorporating a 32-by-64 processing array running at 250 MHz with 1 TOp/s peak throughput in 8 bit mode. The accelerator is capable of processing regular convolutional layers and dilated convolutions in combination with pooling and upsampling. For a semantic segmentation network with 108.5 GOp/frame, our FPGA implementation achieves a throughput of 7.0 FPS in the 8 bit accurate mode and upto 11.2 FPS in the 4 bit mode corresponding to 760.1 GOp/s and 1,218 GOp/s effective throughput, respectively. Finally, we compare the novel design to classical multiplier-based approaches in terms of FPGA utilization and power consumption. Our novel multiply-accumulate engines designed for the optimized number representation uses 9 % less logical elements while allowing double throughput compared to a classical implementation. Moreover, a measurement shows 25% reduction of power consumption at same throughput. Therefore, our flexible design offers a solution to the trade-off between energy efficiency, accuracy, and high throughput.} } @article {ubi_ieeepc2019, title = {Unity in Diversity: Sampling Strategies in Wearable Photoplethysmography}, journal = {IEEE Pervasive Computing}, journalurl = {https://www.computer.org/pervasive-computing/}, volume = {18}, number = {3}, year = {2019}, month = {11/2019}, pages = {63 -- 69}, doi = {10.1109/MPRV.2019.2926613}, issn = {1536-1268}, abstracturl={\url{http://ieeexplore.ieee.org/}}, abstract = {Photoplethysmography optically measures the pulsating blood volume flow in the human skin so that primary vital signs such as the heart rate can be determined. Most known is this sensing principle from the mysterious illumination that can sometimes be seen at the back of fitness trackers and smartwatches. Since powering these high-intensity light puts a large dent into a wearable's energy budget, this paper delves into the sampling schemes and strategies used by current off-the-shelf wearables to save energy and yet obtain good readings. As it turns out, the devices are following very different approaches.}, author = {Florian Wolling and Simon Heimes and Van Laerhoven, Kristof}, } @Article{ubi_na2019, author="Schulze-Bonhage, A. and Boettcher, S. and Glasstetter, M. and Epitashvili, N. and Bruno, E. and Richardson, M. and v. Laerhoven, K. and Duempelmann, M.", title="Mobiles Anfallsmonitoring bei Epilepsiepatienten", journal="Der Nervenarzt", year="2019", month="Oct", day="31", abstract="Wearables stossen bei Epilepsiepatienten und behandelnden Aerzten auf ein grosses Interesse zur Erfassung der Anfallsfrequenz und Warnung bei Anfaellen, darueber hinaus auch zur Erkennung anfallsassoziierter Gefaehrdungen von Patienten, zur differenzialdiagnostischen Einschaetzung seltener Anfallsformen und zur Vorhersage von Perioden mit erhoehter Anfallswahrscheinlichkeit. Akzelerometrie, Elektromyographie, Herzfrequenzmessungen und weitere Verfahren zur Messung autonomer Parameter werden hierbei zur Erfassung klinischer Anfallssymptome eingesetzt. Gegenwaertig wird ein klinischer Einsatz zur Erfassung naechtlicher motorischer Anfaelle moeglich. In der Uebersicht werden verfuegbare Geraete, Daten zur Leistungsfaehigkeit bei der Dokumentation von Anfaellen, aktuelle Einsatzmoeglichkeiten sowie Entwicklungen in der Datenanalyse kritisch dargestellt und diskutiert.", issn="1433-0407", doi="10.1007/s00115-019-00822-x" } @ARTICLE{ubi_fip2019, AUTHOR={Wenzel, Mario and Geelen, Anouk and Wolters, Maike and Hebestreit, Antje and Van Laerhoven, Kristof and Lakerveld, Jeroen and Andersen, Lene Frost and van't Veer, Pieter and Kubiak, Thomas}, TITLE={The Role of Self-Control and the Presence of Enactment Models on Sugar-Sweetened Beverage Consumption: A Pilot Study}, JOURNAL={Frontiers in Psychology}, journalurl={\url{http://journal.frontiersin.org/journal/psychology}}, abstracturl={\url{http://journal.frontiersin.org/article/10.3389/fpsyg.2019.01511/abstract}}, VOLUME={10}, YEAR={2019}, PAGES={1511}, URL={http://www.frontiersin.org/mobile_and_ubiquitous_computing/10.3389/fpsyg.2019.01511/abstract}, DOI={10.3389/fpsyg.2019.01511}, ISSN={1664-1078} , ABSTRACT={The objective of the present research was to investigate associations of dispositional and momentary self-control and the presence of other individuals consuming SSBs with the consumption frequency of sugar-sweetened beverages (SSBs) in a multi-country pilot study. We conducted an Ambulatory Assessment in which 75 university students (52 females) from four study sites carried smartphones and received prompts six times a day in their everyday environments to capture information regarding momentary self-control and the presence of other individuals consuming SSBs. Multilevel models revealed a statistically significant negative association between dispositional self-control and SSB consumption. Moreover, having more self-control than usual was only beneficial in regard to lower SSB consumption frequency, when other individuals consuming SSBs were not present but not when they were present. The findings support the hypothesis that self-control is an important factor regarding SSB consumption. This early evidence highlights self-control as a candidate to design interventions to promote healthier drinking through improved self-control.} } @y{y18,y={2018}} @inproceedings{ubi_icmi2018, author = {Philip Schmidt and Attila Reiss and Robert Duerichen and Claus Marberger and Kristof Van Laerhoven}, title = {Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection}, booktitle = {20th ACM International Conference on Multimodal Interaction (ICMI ’18)}, year = {2018}, publisher = {ACM Press}, abstract = {Affect recognition aims to detect a person’s affective state based on observables, with the goal to e.g. improve human-computer interaction. Long-term stress is known to have severe implications on wellbeing, which call for continuous and automated stress monitoring systems. However, the affective computing community lacks commonly used standard datasets for wearable stress detection which a) provide multimodal high-quality data, and b) include multiple affective states. Therefore, we introduce WESAD, a new publicly available dataset for wearable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study. The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature, and three- axis acceleration. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement). In addition, self-reports of the subjects, which were obtained using several established questionnaires, are contained in the dataset. Furthermore, a benchmark is created on the dataset, using well-known features and standard machine learning methods. Considering the three- class classification problem (baseline vs. stress vs. amusement), we achieved classification accuracies of up to 80 %. In the binary case (stress vs. non-stress), accuracies of up to 93 % were reached. Finally, we provide a detailed analysis and comparison of the two device locations (chest vs. wrist) as well as the different sensor modalities.}, doi = {10.1145/3242969.3242985}, isbn = {978-1-4503-5692-3/18/10} } @inproceedings{ubi_iwoar2018a, author = {Florian Wolling and Kristof Van Laerhoven}, title = {Fewer Samples for a Longer Life Span:Towards Long-Term Wearable PPG Analysis}, booktitle = {Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction}, year = {2018}, publisher = {ACM Press}, abstract = {Photoplethysmography (PPG) sensors have become a prevalent feature included in current wearables, as the cost and sizeof current PPG modules have dropped significantly. Research in the analysis of PPG data has recently expanded beyond the fast and accurate characterization of heart rate, into the adaptive handling of artifacts within the signal and even the capturing of respiration rate. In this paper, we instead explore using state-of-the-art PPG sensor modules for long-term wearable deployment and the observation of trends over minutes, rather than seconds. By focusing specifically on lowering the sampling rate and via analysis of the spectrum of frequencies alone, our approach minimizes the costly illumination-based sensing and can be used to detect the dominant frequencies of heart rate and respiration rate, but also enables to infer on activity of the sympathetic nervous system. We show in two experiments that such detections and measurements can still be achieved at low sampling rates down to10 Hz, within a power-efficient platform. This approach enables miniature sensor designs that monitor average heart rate, respiration rate, and sympathetic nerve activity over longer stretches of time.}, doi = {10.1145/3266157.3266209} } @inproceedings{ubi_arxiv2018, author = {Philip Schmidt and Attila Reiss and Robert Duerichen and Kristof Van Laerhoven}, title = {Wearable affect and stress recognition: A review}, booktitle = {arXiv:1811.08854}, year = {2018}, publisher = {}, abstract = {Affect recognition aims to detect a person’s affective state based on observables, with the goal to e.g. provide reasoning for decision making or support mental wellbeing. Recently, besides approaches based on audio, visual or text information, solutions relying on wearable sensors as observables (recording mainly physiological and inertial parameters) have received increasing attention. Wearable systems offer an ideal platform for long-term affect recognition applications due to their rich functionality and form factor. However, existing literature lacks a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods, and best practices of wearable affect and stress recognition. We summarise psychological models, and detail affect-related physiological changes and their measurement with wearables. We outline lab protocols eliciting affective states, and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain, and review common approaches to preprocessing, feature extraction, and classification. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field of affect recognition to conduct and evaluate user studies and develop wearable systems.}, doi = {} } @inproceedings{ubi_iwoar2018b, author = {Alexander Hoelzemann and Kristof Van Laerhoven}, title = {Using Wrist-Worn Activity Recognition for Basketball Game Analysis}, booktitle = {Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction}, year = {2018}, publisher = {ACM Press}, abstract = {Game play in the sport of basketball tends to combine highly dynamic phases in which the teams strategically move across the field, with specific actions made by individual players. Analysis of basketball games usually focuses on the locations of players at particular points in the game, whereas the capture of what actions the players were performing remains underrepresented. In this paper, we present an approach that allows to monitor players' actions during a game, such as dribbling, shooting, blocking, or passing, with wrist-worn inertial sensors. In a feasibility study, inertial data from a sensor worn on the wrist were recorded during training and game sessions from three players. We illustrate that common features and classifiers are able to recognize short actions, with overall accuracy performances around 83.6% (k-Nearest-Neighbor) and 87.5% (Random Forest). Some actions, such as jump shots, performed well (± 95% accuracy), whereas some types of dribbling achieving low (± 44%) recall.}, doi = {10.1145/3266157.3266217} } @inproceedings{ubi_iwoar2018c, author = {Jochen Kempfle and Kristof Van Laerhoven}, title = {Respiration Rate Estimation with Depth Cameras: An Evaluation of Parameters}, booktitle = {Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction}, year = {2018}, publisher = {ACM Press}, abstract = {Depth cameras have been known to be capable of picking up the small changes in distance from users' torsos, to estimate respiration rate. Several studies have shown that under certain conditions, the respiration rate from a non-mobile user facing the camera can be accurately estimated from parts of the depth data. It is however to date not clear, what factors might hinder the application of this technology in any setting, what areas of the torso need to be observed, and how readings are affected for persons at larger distances from the RGB-D camera. In this paper, we present a benchmark dataset that consists of the point cloud data from a depth camera, which monitors 7 volunteers at variable distances, for variable methods to pin-point the person's torso, and at variable breathing rates. Our findings show that the respiration signal's signal-to-noise ratio becomes debilitating as the distance to the person approaches 4 metres, and that bigger windows over the person's chest work particularly well. The sampling rate of the depth camera was also found to impact the signal's quality significantly. }, doi = {10.1145/3266157.3266208} } @inproceedings{ubi_ubi2018ws, author = {Philip Schmidt and Attila Reiss and Robert Duerichen and Kristof Van Laerhoven}, title = {Labelling Affective States ”in the wild”: Practical Guidelines and Lessons Learned}, booktitle = {UbiComp/ISWC ’18 Adjunct}, year = {2018}, publisher = {ACM Press}, abstract = {In affective computing (AC) field studies it is impossible to obtain an objective ground truth. Hence, self-reports in form of ecological momentary assessments (EMAs) are frequently used in lieu of ground truth. Based on four paradigms, we formulate practical guidelines to increase the accuracy of labels generated via EMAs. In addition, we detail how these guidelines were implemented in a recent AC field study of ours. During our field study, 1081 EMAs were collected from 10 subjects over a duration of 148 days. Based on these EMAs, we perform a qualitative analysis of the effectiveness of our proposed guidelines. Furthermore, we present insights and lessons learned from the field study.}, doi = {10.1145/3267305.3267551} } @inproceedings{ubi_ubi2018cpd, author = {Attila Reiss and Ina Indlekofer and Philip Schmidt and Kristof Van Laerhoven}, title = {PPG-based Heart Rate Estimation with Time-Frequency Spectra: A Deep Learning Approach}, booktitle = {UbiComp/ISWC ’18 Adjunct}, year = {2018}, publisher = {ACM Press}, abstract = {PPG-based continuous heart rate estimation is challenging due to the effects of physical activity. Recently, methods based on time-frequency spectra emerged to compen- sate motion artefacts. However, existing approaches are highly parametrised and optimised for specific scenarios. In this paper, we first argue that cross-validation schemes should be adapted to this topic, and show that the generalisation capabilities of current approaches are limited. We then introduce deep learning, specifically CNN-models, to this domain. We investigate different CNN-architectures (e.g. the number of convolutional layers, applying batch normalisation, or ensemble prediction), and report insights based on our systematic evaluation on two publicly available datasets. Finally, we show that our CNN-based approach performs comparably to classical methods.}, doi = {10.1145/3267305.3274176} } @article{ubi_mdpi_inf2018b, author = {Mummadi, Chaithanya Kumar and Leo, Frederic Philips Peter and Verma, Keshav Deep and Kasireddy, Shivaji and Scholl, Philipp M. and Kempfle, Jochen and Laerhoven, Kristof Van}, title = {Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove}, journal = {Informatics}, volume = {5}, year = {2018}, number = {2}, articlenumber = {28}, journalurl = {http://www.mdpi.com/2227-9709/5/2/28}, issn = {2227-9709}, abstract = {This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user's hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.}, doi = {10.3390/informatics5020028} } @article{ubi_mdpi_inf2018a, author = {Boettcher, Sebastian and Scholl, Philipp M and Van Laerhoven, Kristof}, title = {Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach}, journal = {Informatics}, volume = {5}, year = {2018}, number = {2}, number = {16}, journalurl = {http://www.mdpi.com/2227-9709/5/2/16}, issn = {2227-9709}, abstract = {Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user's physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods.}, doi = {10.3390/informatics5020016} } @article{ubi_iop2018, doi = {10.1088/1742-6596/1052/1/012091}, url = {https://doi.org/10.1088%2F1742-6596%2F1052%2F1%2F012091}, year = 2018, month = {jul}, publisher = {{IOP} Publishing}, volume = {1052}, pages = {012091}, author = {P. Mehne and P. Scholl and A. Rudmann and M. Kroener and K. Van Laerhoven and P. Woias}, title = {Evaluation of an Impact Spring-Coil-Magnet System with 3D-Printed Setup}, journal = {Journal of Physics: Conference Series} } @inproceedings{ubi_ewsn2018, author = {Hermeto, Rodrigo Teles and Gallais, Antoine and Laerhoven, Kristof Van and Tholeyre, Fabrice}, title = {Passive Link Quality Estimation for Accurate and Stable Parent Selection in Dense 6TiSCH Networks}, booktitle = {Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks}, series = {EWSN \&\#8217;18}, year = {2018}, isbn = {978-0-9949886-2-1}, location = {Madrid, Spain}, pages = {114--125}, numpages = {12}, url = {http://dl.acm.org/citation.cfm?id=3234847.3234862}, bookurl = {https://dl.acm.org/citation.cfm?id=3234847}, acmid = {3234862}, publisher = {ACM Press}, address = {USA}, keywords = {6TiSCH, IEEE802.15.4-2015-TSCH, IoT, Link Quality Estimation}, abstract = {Industrial applications are increasingly demanding more low-power operations, deterministic communications and end-to-end reliability that approaches 100%. Recent standardization efforts focus on exploiting slow channel hopping while properly scheduling the transmissions to provide a strict Quality of Service for the Industrial Internet of Things (IIoT). By keeping nodes time-synchronized and by employing a channel hopping approach, IEEE 802.15.4-TSCH (Time-Slotted Channel Hoping) aims at providing high-level network reliability. For this, however, we need to construct an accurate schedule, able to exploit reliable paths. In particular, radio links with high Packet Error Rate should not be exploited since they are less energy-efficient (more retransmissions are required) and they negatively impact the reliability. In this work, we take advantage of the continuously advertisement packets transmitted by the nodes to infer the link quality. We argue that the reception rate of broadcast packets provide means to estimate the link quality to different neighbors, even when the data packets use different, collision-free transmission opportunities. Our experiments on a large-scale platform highlight that our approach improves the convergence delay, identifying the best routes to the sink during the bootstrapping (or reconverging) phase without adding any extra control packet.} } @inproceedings{ubi_wristsense2018, author = {Kempfle, Jochen and Van Laerhoven, Kristof}, title = {PresentPostures: A Wrist and Body Capture Approach for Augmenting Presentations}, booktitle = {Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on}, publisher = {{IEEE}}, year = {2018}, abstract = {Capturing and digitizing all nuances during presentations is notoriously difficult. At best, digital slides tend to be combined with audio, while video footage of the presenter's body language often turns out to be either too sensitive, occluded, or hard to achieve for common lighting conditions. If presentations require capturing what is written on the whiteboard, more expensive setups are usually needed. In this paper, we present an approach that complements the data from a wrist-worn inertial sensor with depth camera footage, to obtain an accurate posture representation of the presenter. A wearable inertial measurement unit complements the depth footage by providing more accurate arm rotations and wrist postures when the depth images are occluded, whereas the depth images provide an accurate full-body posture for indoor environments. In an experiment with 10 volunteers, we show that posture estimates from depth images and inertial sensors complement each other well, resulting in far less occlusions and tracking of the wrist with an accuracy that supports capturing sketches.}, doi = {10.1109/PERCOMW.2018.8480155} } @inproceedings{ubi_eusipco2018, author = {Christian Reich and Ahmad Mansour and Kristof van Laerhoven}, title = {Embedding Intelligent Features for Vibration-Based Machine Condition Monitoring}, booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)}, publisher = {{IEEE}}, year = {2018}, abstract = {Today’s demands regarding workpiece quality in cutting machine tool processing require automated monitoring of both machine condition and the cutting process. Currently, best-performing monitoring approaches rely on high-frequency acoustic emission (AE) sensor data and definition of advanced features, which involve complex computations. This approach is challenging for machine monitoring via embedded sensor systems with constrained computational power and energy budget. To cope with constrained energy, we rely on data recording with microelectromechanical system (MEMS) vibration sensors, which rely on lower-frequency sampling. To clarify whether these lower-frequency signals bear information for typical machine monitoring prediction tasks, we evaluate data for the most generic machine monitoring task of tool condition monitoring (TCM). To cope with computational complexity of advanced features, we introduce two intelligent preprocessing algorithms. First, we split non-stationary signals of recurrent structure into similar segments. Then, we identify most discriminative spectral differences in the segmented signals that allow for best separation of classes for the given TCM task. Subsequent feature extraction only in most relevant signal segments and spectral regions enables high expressiveness even for simple features. Extensive evaluation of the outlined approach on multiple data sets of different combinations of cutting machine tools, tool types and workpieces confirms its sensibility. Intelligent preprocessing enables reliable identification of stationary segments and most discriminative frequency bands. With subsequent extraction of simple but tailor-made features in these spectral-temporal regions of interest (RoIs), TCM typically framed as multi feature classification problem can be converted to a single feature threshold comparison problem with an average F1 score of 97.89%. }, doi = {10.23919/EUSIPCO.2018.8553525} } @y{y17,y={2017}} @inproceedings{ubi_iswc2017a, author = {Wolling, Florian and Scholl, Philipp M. and Reindl, Leonhard M. and Van Laerhoven, Kristof}, title = {Combining Capacitive Coupling with Conductive Clothes:Towards Resource-Efficient Wearable Communication }, booktitle = {Proceedings of the 2017 {ACM} International Symposium on Wearable Computers, {ISWC} 2017, Maui, Hawaii, September 11-15, 2017}, publisher = {{ACM}}, year = {2017}, abstract = {Traditional intra-body communication approaches mostly rely on either fixed cable joints embedded in clothing, or on wireless radio transmission that tends to reach beyond the body. Situated between these approaches is body-coupled communication, a promising yet less-explored method that transmits information across the user's skin. We propose a novel body-coupled communication approach that simplifies the physical layer of data transmission via capacitive coupling between wearable systems with conductive fabrics: This layer provides a stable reference potential for the feedback path in proximity to the attached wearables on the human body, to cancel the erratic dependency on the environmental ground, and to increase the communications' reliability. Evaluation of our prototype shows significant increases in signal quality, due to reduced attenuation and noise. Requirements on hardware and, subsequently, energy consumption, cost, and implementation effort are reduced as well.}, isbn = {978-1-4503-5188-1/17/09}, doi = {10.1145/3123021.3123059}, } @inproceedings{ubi_iswc2017b, author = {Scholl, Philipp M. and Van Laerhoven, Kristof}, title = {On the Statistical Properties of Body-Worn Inertial Motion Sensor Data for Identifying Sensor Modality }, booktitle = {Proceedings of the 2017 {ACM} International Symposium on Wearable Computers, {ISWC} 2017, Maui, Hawaii, September 11-15, 2017}, publisher = {{ACM}}, year = {2017}, abstract = {Interpreting datasets containing inertial data (acceleration, rate-of-turn, magnetic flux) requires a description of the datasets itself. Often this description is unstructured, stored as a convention or simply not available anymore. In this note, we argue that each modality exhibits particular statistical properties, which allows to reconstruct it solely from the sensor's data. To investigate this, tri-axial inertial sensor data from five publicly available datasets were analysed. Three statistical properties: mode, kurtosis, and number of modes are shown to be sufficient for classification - assuming the sampling rate and sample format are known, and that both acceleration and magnetometer data is present. While those assumption hold, 98% of all 1003 data points were correctly classified.}, isbn = {978-1-4503-5188-1/17/09}, doi = {10.1145/3123021.3123048}, } @inproceedings{ubi_mum2017, author = {Kempfle, Jochen and Van Laerhoven, Kristof}, title = {Human Posture Capture and Editing from Heterogeneous Modalities}, booktitle = {Proceedings of the 16th International Conference on Mobile and Ubiquitous Multimedia}, series = {MUM '17}, year = {2017}, isbn = {978-1-4503-5378-6}, location = {Stuttgart, Germany}, pages = {489--494}, numpages = {6}, url = {http://doi.acm.org/10.1145/3152832.3157812}, doi = {10.1145/3152832.3157812}, acmid = {3157812}, publisher = {ACM}, address = {New York, NY, USA}, abstract = {Estimating human pose can be done with a variety of technologies. Models are typically derived from a motion capturing system that aims at generating a skeleton model as accurate and fast as possible. To date, a large variety of solutions is available to obtain capture data, often either from the environment, such as from depth cameras, or person-based, such as by body-worn inertial sensors. There are, however, few frameworks that are able to combine different types of human motion data into one environment. To this end, we present a modular and open architecture that allows specifically to integrate different capture sources and different filters to capture, process, and fuse these, in real time, into a human posture model. We show how our proposed solution leads to a fast and flexible way to deal with multiple motion capture modalities, and how such a framework can be used for editing and combining postures.}, keywords = {human pose estimation, inertial measurement, motion capture}, } @inproceedings{ubi_ubi2017ws, author = {Scholl, Philipp M. and Van Laerhoven, Kristof}, title = { Lessons Learned From Designing an Instrumented Lighter for Assessing Smoking Status }, booktitle = {Ubicomp 2017 Adjunct Proceedings}, publisher = {{ACM}}, year = {2017}, abstract = {Assessing smoking status from wearable sensors can potentially create novel cessation therapies for the global smoking epidemic, without requiring users to regularly fill in questionnaires to obtain their smoking data. In this paper we discuss several design iterations of an instrumented cigarette lighter the records when it is used, to provide the ground-truth for detection from sensor signals measured at the human body, or to provide an alternative low-delay detection mechanism for smoking.}, isbn = {978-1-4503-5190-4}, doi = {10.1145/3123024.3124397}, pages = {1016--1021}, } @inproceedings{ubi_ubi2017hws, author = {Nobuo Kawaguchi and Nobuhiko Nishio and Daniel Roggen and Sozo Inoue and Susanna Pirttikangas and Kristof Van Laerhoven}, abstract = {Technological advances enable the inclusion of miniature sensors (e.g., accelerometers, gyroscopes) on a variety of wearable/portable information devices. Most current devices utilize these sensors for simple orientation and gesture recognition only. However, in the future the recognition of more complex and subtle human behaviors from these sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpuses and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. As a special topic this year, we wish to reflect on the challenges and possible approaches to recognize situations, events or activities outside of a statically pre-defined pool, which is the current state of the art, and instead adopt an open-ended view on activity and context awareness. Following the huge success of previous years, we are further planning to share these experiences of current research on human activity corpus and their applications among the researchers and the practitioners and to have a deep discussion on the future of activity sensing, in particular towards open-ended contextual intelligence.}, title = {5th Int. workshop on human activity sensing corpus and applications {(HASCA):} towards open-ended context awareness}, booktitle = {Adjunct Proceedings of the 2017 {ACM} International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 {ACM} International Symposium on Wearable Computers, UbiComp/ISWC 2017, Maui, HI, USA, September 11-15, 2017}, pages = {530--536}, year = {2017}, url = {http://doi.acm.org/10.1145/3123024.3124410}, doi = {10.1145/3123024.3124410}, } @inproceedings{ubi_iwoar2017a, author = {Van Laerhoven, Kristof and Wenzel, Mario and Geelen, Anouk and Huebel, Christopher and Wolters, Maike and Hebestreit, Antje and Andersen, Lene Frost and van't Veer, Pieter and Kubiak, Thomas}, title = {Experiences from a Wearable-Mobile Acquisition System for Ambulatory Assessment of Diet and Activity }, booktitle = {Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction}, abstract = {Public health trends are currently monitored and diagnosed based on large studies that often rely on pen-and-paper data methods that tend to require a large collection campaign. With the pervasiveness of smart-phones and -watches throughout the general population, we argue in this paper that such devices and their built-in sensors can be used to capture such data more accurately with less of an effort. We present a system that targets a pan-European and harmonised architecture, using smartphones and wrist-worn activity loggers to enable the collection of data to estimate sedentary behavior and physical activity, plus the consumption of sugar-sweetened beverages. We report on a unified pilot study across three countries and four cities (with different languages, locale formats, and data security and privacy laws) in which 83 volunteers were asked to log beverages consumption along with a series of surveys and longitudinal accelerometer data. Our system is evaluated in terms of compliance, obtained data, and first analyses.}, series = {WOAR '17}, year = {2017}, isbn = {978-1-4503-5223-9/17/09}, doi = {10.1145/3134230.3134239}, location = {Rostock, Germany}, publisher = {ACM}, address = {New York, NY, USA}, } @inproceedings{ubi_iwoar2017b, author = {Kumar Mummadi, Chaithanya and Philips Peter Leo, Frederic and Deep Verma, Keshav, and Kasireddy, Shivaji and Scholl, Philipp Marcel and Van Laerhoven, Kristof}, title = {Real-time Embedded Recognition of Sign Language Alphabet Fingerspelling in an IMU-Based Glove }, booktitle = {Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction}, abstract = {Data gloves have numerous applications, including enabling novel human-computer interaction and automated recognition of large sets of gestures, such as those used for sign language. For most of these applications, it is important to build mobile and self-contained applications that run without the need for frequent communication with additional services on a back-end server. We present in this paper a data glove prototype, based on multiple small Inertial Measurement Units (IMUs), with a glove-embedded classifier for the french sign language. In an extensive set of experiments with 57 participants, our system was tested by repeatedly fingerspelling the French Sign Language(LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, with all detections performed on the glove within 63 milliseconds.}, series = {WOAR '17}, year = {2017}, isbn = {978-1-4503-5223-9/17/09}, doi = {10.1145/3134230.3134236}, location = {Rostock, Germany}, publisher = {ACM}, address = {New York, NY, USA}, } @inproceedings{ubi_iwoar2017c, author = {Böttcher, Sebastian and Scholl, Philipp Marcel and Van Laerhoven, Kristof}, title = {Detecting Process Transitions from Wearable Sensors: An Unsupervised Labeling Approach}, booktitle = {Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction}, abstract = {Authoring protocols for manual tasks such as following recipes, manufacturing processes, or laboratory experiments requires a significant effort. This paper presents a system that estimates individual procedure transitions from the user's physical movement and gestures recorded with inertial motion sensors. This is combined with egocentric or external video recordings, allowing for an efficient review of video archives. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised clustering algorithms, the automatically generated marks are compared to human-expert created labels on publicly available datasets. Additionally, we tested the approach on a novel data set in a manufacturing lab environment, describing an existing sequential manufacturing process.}, series = {WOAR '17}, year = {2017}, isbn = {978-1-4503-5223-9/17/09}, doi = {10.1145/3134230.3134233}, location = {Rostock, Germany}, publisher = {ACM}, address = {New York, NY, USA}, note = {best paper runner up}, } @article {ubi_ieeepc2017, title = {What Will We Wear After Smartphones? }, journal = {IEEE Pervasive Computing}, journalurl = {https://www.computer.org/pervasive-computing/}, volume = {16}, number = {4}, year = {2017}, month = {10/2017}, pages = {80 -- 85}, doi = {10.1109/MPRV.2017.3971124}, issn = {1536-1268}, abstracturl={\url{http://ieeexplore.ieee.org/}}, abstract = {With wearable computing research recently passing the 20-year mark, this survey looks back at how the field developed and explores where it’s headed. According to the authors, wearable computing is entering its most exciting phase yet, as it transitions from demonstrations to the creation of sustained markets and industries, which in turn should drive future research and innovation.}, author = {Amft, Oliver and Van Laerhoven, Kristof}, } @y{y16,y={2016}} @inproceedings{es_ah2016, author = {Theiss, Marian and Scholl, Philipp M. and Van Laerhoven, Kristof}, title = {Predicting Grasps with a Wearable Inertial and EMG Sensing Unit for Low-Power Detection of In-Hand Objects}, booktitle = {Proceedings of the 7th Augmented Human International Conference 2016}, bookurl={\url{http://www.augmented-human.com/}}, series = {AH '16}, year = {2016}, isbn = {978-1-4503-3680-2}, location = {Geneva, Switzerland}, pages = {4:1--4:8}, articleno = {4}, numpages = {8}, url = {http://doi.acm.org/10.1145/2875194.2875207}, doi = {10.1145/2875194.2875207}, acmid = {2875207}, publisher = {ACM}, address = {New York, NY, USA}, abstract = {Detecting the task at hand can often be improved when it is also known what object the user is holding. Several sensing modalities have been suggested to identify handheld objects, from wrist-worn RFID readers to cameras. A critical obstacle to using such sensors, however, is that they tend to be too power hungry for continuous usage. This paper proposes a system that detects grasping using first inertial sensors and then Electromyography (EMG) on the forearm, to then selectively activate the object identification sensors. This three-tiered approach would therefore only attempt to identify in-hand objects once it is known a grasping has occurred. Our experiments show that high recall can be obtained for grasp detection, 95% on average across participants, with the grasping of lighter and smaller objects clearly being more difficult.}, keywords = {inertial sensing, wearable EMG, wrist-worn object detection}, } @inproceedings{es_iot2016, author = {Schwabe, Nils and Scholl, Philipp M. and Van Laerhoven, Kristof}, title = {An Ad-Hoc Capture System for Augmenting Non-Digital Water Meters}, booktitle = {Proceedings of the 6th International Conference on the Internet of Things}, series = {IoT'16}, year = {2016}, isbn = {978-1-4503-4814-0}, location = {Stuttgart, Germany}, pages = {25--33}, numpages = {9}, url = {http://doi.acm.org/10.1145/2991561.2991576}, doi = {10.1145/2991561.2991576}, acmid = {2991576}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {digital water meter, eco-feedback, sensors}, abstract = {Deriving more detailed insights into one's ecological footprint is a premise to reduce one's individual environmental impact. Personal water consumption contributes significantly to this impact, but remains hard to quantify individually unless digital meters are installed. In this paper, we present a dual-sensing approach to retro-fit common water meters with a wireless sensor unit that is able to capture an individual's water usage, and digitally forward it over the home's WiFi network. Utilizing active infrared distance sensing or sensing magnetic flux, it is possible to measure water consumption with an accuracy below 0.1l on commonly installed meters. With a continuous power consumption (assuming a daily water consumption of 2 hours) of less than 20 mW, the system can be provide real-time feedback to home-owners, office workers and people sharing such a retro-fitted water supply.}, } @inproceedings{es_mum2016, author = {Scholl, Philipp M. and van Laerhoven, Kristof}, title = {Remind: Towards a Personal Remembrance Search Engine for Motion Augmented Multi-media Recordings}, booktitle = {Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia}, series = {MUM '16}, year = {2016}, isbn = {978-1-4503-4860-7}, location = {Rovaniemi, Finland}, pages = {363--364}, numpages = {2}, url = {http://doi.acm.org/10.1145/3012709.3016074}, doi = {10.1145/3012709.3016074}, acmid = {3016074}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {encoding, indexing, motion, multi-media, querying}, abstract = {A searchable database of multi-media recordings provides a way to augment one's memory. This database might contain video, audio and motion data, which is indexed to allow for quick searches. Ultimately, queries for similarity on each recorded modality would be supported. For example video sequencing showing similar objects or comparable sequences of gestures can be retrieved. An important aspect of this challenge is how to encode such multi-modal data, and how to make it searchable. One approach, based on a multi-media container format, is proposed in this paper together with an architecture to allow for similarity queries on multiple modalities.} } @article{es_ijmhci2016, author = {Schneegass, Stefan and Thomas Olsson and Sven Mayer and Kristof van Laerhoven}, title = {Mobile Interactions Augmented by Wearable Computing: A Design Space and Vision}, journal = {IJMHCI}, volume = {8}, month = {August}, year = {2016}, number = {4}, pages = {104--114}, url = {http://www.igi-global.com/article/mobile-interactions-augmented-by-wearable-computing/162147}, issn = {2409-9287}, abstract = {Wearable computing has a huge potential to shape the way we interact with mobile devices in the future. Interaction with mobile devices is still mainly limited to visual output and tactile finger-based input. Despite the visions of next-generation mobile interaction, the hand-held form factor hinders new interaction techniques becoming commonplace. In contrast, wearable devices and sensors are intended for more continuous and close-to-body use. This makes it possible to design novel wearable-augmented mobile interaction methods – both explicit and implicit. For example, the EEG signal from a wearable breast strap could be used to identify user status and change the device state accordingly (implicit) and the optical tracking with a head-mounted camera could be used to recognize gestural input (explicit). In this paper, the authors outline the design space for how the existing and envisioned wearable devices and sensors could augment mobile interaction techniques. Based on designs and discussions in a recently organized workshop on the topic as well as other related work, the authors present an overview of this design space and highlight some use cases that underline the potential therein.}, doi = {10.4018/IJMHCI.2016100106} } @Inbook{es_lifelogging2016, author="Dietrich, Manuel and van Laerhoven, Kristof", editor="Selke, Stefan", title="Reflect Yourself!", bookTitle="Lifelogging: Digital self-tracking and Lifelogging - between disruptive technology and cultural transformation", year="2016", pages = {213--233}, publisher = {Springer Nature}, address="Wiesbaden", pages="213--233", isbn="978-3-658-13137-1", doi="10.1007/978-3-658-13137-1_12", url="http://dx.doi.org/10.1007/978-3-658-13137-1_12", abstract = {In this paper we introduce an interdisciplinary investigation into technology of wearable activity recognition and its applications for self-tracking and lifelogging. Wearable activity recognition are computer systems capable of automatically detecting human actions. Using these devices for self-tracking provides the users with a new perspective on their actions. Thus people can reflect on their actions in a new way. We work on the topic of wearable activity recognition in an interdisciplinary way, both with a theoretical analytic direction and a concrete system design perspective. The theoretical part of this article is about understanding how people relate to their actions using an activity recognition lifelogging device. It is based on the philosophical theory of action. For the concrete design perspective of wearable activity recognition, we introduce two cases from our current design practice. We bridge the theoretical thoughts and the practical perspective by introducing the (critical) design theory. Based on that, opportunity and limits for self-tracking and self-reflection are the results of the interdisciplinary approach.} } @inproceedings{es_ubicompws, author = {Kawaguchi, Nobuo and Nishio, Nobuhiko and Roggen, Daniel and Inoue, Sozo and Pirttikangas, Susanna and van Laerhoven, Kristof}, title = {4th Workshop on Human Activity Sensing Corpus and Applications: Towards Open-ended Context Awareness}, booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct}, series = {UbiComp '16}, year = {2016}, isbn = {978-1-4503-4462-3}, location = {Heidelberg, Germany}, pages = {690--695}, numpages = {6}, url = {http://doi.acm.org/10.1145/2968219.2968294}, doi = {10.1145/2968219.2968294}, acmid = {2968294}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {activity recognition, large scale human activity sensing corpus, mobile sensor, open-ended activity/context recognition, participatory sensing, wearable computing}, } @inproceedings{es_ubicompadj2016, author = {Scholl, Philipp M. and Van Laerhoven, Kristof}, title = {A Multi-Media Exchange Format for Time-Series Dataset Curation}, booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication}, series = {UbiComp '16 Adjunct}, year = {2016}, isbn = {}, location = {Heidelberg, Germany}, pages = {}, numpages = {7}, url = "http://dx.doi.org/10.1145/2968219.2968278", doi = "10.1145/2968219.2968278", publisher = {ACM}, abstract = {Exchanging data as comma-separated values (CSV) is slow, cumbersome and error-prone. Especially for time- series data, which is common in Activity Recognition, syn- chronizing several independently recorded sensors is chal- lenging. Adding second level evidence, like video record- ings from multiple angles and time-coded annotations, fur- ther complicates the matter of curating such data. A possible alternative is to make use of standardized multi- media formats. Sensor data can be encoded in audio for- mat, and time-coded information, like annotations, as sub- titles. Video data can be added easily. All this media can be merged into a single container file, which makes the is- sue of synchronization explicit. The incurred performance overhead by this encoding is shown to be negligible and compression can be applied to optimize storage and trans- mission overhead.}, } @Inbook{es_embedded2016, author="Van Laerhoven, Kristof and Scholl, Philipp M.", editor="Szewczyk, Roman and Ka{\v{s}}telan, Ivan and Temerinac, Miodrag and Barak, Moshe and Sruk, Vlado", title="Interrupts Become Features: Using On-Sensor Intelligence for Recognition Tasks", bookTitle="Embedded Engineering Education", year="2016", publisher="Springer International Publishing", address="Cham", pages="171--185", isbn="978-3-319-27540-6", doi="10.1007/978-3-319-27540-6_12", url="http://dx.doi.org/10.1007/978-3-319-27540-6_12", abstract = {Wearable sensors have traditionally been designed around a micro controller that periodically reads values from attached sensor chips, before analyzing and forwarding data. As many off-the-shelf sensor chips have become smaller and widespread in consumer appliances, the way they are interfaced has become digital and more potent. This paper investigates the impact of using such chips that are not only smaller and cheaper as their predecessors, but also come with an arsenal of extra processing and detection capabilities, built in the sensor package. A case study with accompanying experiments using two MEMS accelerometers, show that using these capabilities can cause significant reductions in resources for data acquisition, and could even support basic recognition tasks.} } @y{y15,y={2015}} @inproceedings {es_ubicomp2015, title = {Wearables in the Wet Lab: A Laboratory System for Capturing and Guiding Experiments}, author = {Scholl, Philipp M. and Wille, Matthias and Van Laerhoven, Kristof}, booktitle = {The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015)}, bookurl={\url{http://www.ubicomp.org/ubicomp2015}}, year = {2015}, month = {09/2015}, pages = {}, publisher = {ACM Press}, organization = {ACM Press}, address = {Osaka, Japan}, abstract = {Wet Laboratories are highly dynamic, shared environments full of tubes, racks, compounds, and dedicated machinery. The recording of experiments, despite the fact that several ubiquitous computing systems have been suggested in the past decades, still relies predominantly on hand-written notes. Similarly, the information retrieval capabilities inside a laboratory are limited to traditional computing interfaces, which due to safety regulations are sometimes not usable at all. In this paper, Google Glass is combined with a wrist-worn gesture sensor to support Wetlab experimenters. Taking "in-situ" documentation while an experiment is performed, as well as contextualizing the protocol at hand can be implemented on top of the proposed system. After an analysis of current practices and needs through a series of explorative deployments in wet labs, we motivate the need for a wearable hands-free system, and introduce our specific design to guide experimenters. Finally, using a study with 22 participants evaluating the system on a benchmark DNA extraction experiment, we explore the use of gesture recognition for enabling the system to track where the user might be in the experiment. }, url={http://dl.acm.org/citation.cfm?id=2807547}, abstracturl={http://www.ubicomp.org}, doi = {10.1145/2750858.2807547} } @proceedings{es_iswc2015, editor = {Kenji Mase and Marc Langheinrich and Daniel Gatica{-}Perez and Kristof Van Laerhoven and Tsutomu Terada}, author = {Kenji Mase and Marc Langheinrich and Daniel Gatica{-}Perez and Kristof Van Laerhoven and Tsutomu Terada}, title = {Proceedings of the 2015 {ACM} International Symposium on Wearable Computers, {ISWC} 2015, Osaka, Japan, September 7-11, 2015}, publisher = {{ACM}}, year = {2015}, url = {http://dl.acm.org/citation.cfm?id=2802083}, isbn = {978-1-4503-3578-2}, timestamp = {Fri, 11 Sep 2015 14:28:26 +0200}, abstract = {Welcome to UbiComp/ISWC 2015, the premier international forum for pervasive, ubiquitous, and wearable computing, which takes place from September 9-11, 2015 in Osaka, Japan. The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015) is the third installment of the merged format between "Pervasive" and "Ubicomp", the two most renowned conferences in the field. As in previous years, it is co-located with the 19th International Symposium on Wearable Computers (ISWC 2015) - the premier forum to present cutting-edge research results in the fields of wearable computing and on-body mobile technologies. ISWC and UbiComp have been co-located with big success since 2013 in Zurich. This year, the two conferences feature two independent technical programs, but offer a single adjunct track, featuring joint posters, demos, workshops, tutorials, and a common doctoral school. As in previous years, ISWC and UbiComp operate as a single event, i.e., attendees are free to attend events from both conferences interchangeably.}, } @inproceedings{es_issnip2015, author = {Vinay Sachidananda and David Noack and Abdelmajid Khelil and Kristof Van Laerhoven and Philipp M. Scholl}, title = {Optimized multi-attribute co-design for maximizing efficiency in Wireless Sensor Networks}, booktitle = {Tenth {IEEE} International Conference on Intelligent Sensors, Sensor Networks and Information Processing, {ISSNIP} 2015, Singapore, April 7-9, 2015}, pages = {1--6}, publisher = {{IEEE}}, isbn = {978-1-4799-8055-0}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7101334}, year = {2015}, doi = {10.1109/ISSNIP.2015.7106931}, abstract = {A key task in Wireless Sensor Networks (WSNs) is to deliver specific information about a spatial phenomenon of interest. However, in WSNs the operating conditions and/or user requirements are often desired to be evolvable, whether driven by changes of the monitored parameters or WSN properties. To this end, few sensor nodes sample the phenomenon and transmit the acquired samples, typically multihop, to the application through a gateway called sink. Accurately representing the physical phenomenon and reliably, timely delivering the user required information comes at the cost of higher energy as additional messages are required. This work proposes a tunable co-design for network optimization to avoid under or over provision of information and interaction of the attributes and their effects on each other. We validate the approach viability through analytical modeling, simulations for a range of requirements.}, } @inproceedings{es_mum2015, title = {Assessing Activity Recognition Feedback in Long-term Psychology Trials}, author = {Manuel Dietrich and Eugen Berlin and Kristof Van Laerhoven}, booktitle = {14th International Conference on Mobile and Ubiquitous Multimedia}, bookurl={\url{http://www.mum-conf.org/2015/}}, month = {12/2015}, publisher = {ACM Press}, organization = {ACM Press}, address = {Linz, Austria}, abstract = {The physical activities we perform throughout our daily lives tell a great deal about our goals, routines, and behavior, and as such, have been known for a while to be a key indicator for psychiatric disorders. This paper focuses on the use of a wrist-watch with integrated inertial sensors. The algorithms that deal with the data from these sensors can automatically detect the activities that the patient performed from characteristic motion patterns. Such a system can be deployed for several weeks continuously and can thus provide the consulting psychiatrist an insight in their patient's behavior and changes thereof. Since these algorithms will never be flawless, however, a remaining question is how we can support the psychiatrist in assigning confidence to these automatic detections. To this end, we present a study where visualizations at three levels from a detection algorithm are used as feedback, and examine which of these are the most helpful in conveying what activities the patient has performed. Results show that just visualizing the classifier's output performs the best, but that user's confidence in these automated predictions can be boosted significantly by visualizing earlier pre-processing steps.}, year = {2015}, note = {honorable mention award}, doi = {10.1145/2836041.2836052}, isbn = {978-1-4503-3605-5}, url = {http://doi.acm.org/10.1145/2836041.2836052}, location = {Linz, Austria}, pages = {121--130}, numpages = {10}, } @inproceedings{es_iwoar2015b, author = {Scholl, Philipp M. and Schultes, Tobias and Van Laerhoven, Kristof}, title = {RFID-based Compound Identification in Wet Laboratories with Google Glass}, booktitle = {Proceedings of the 2Nd International Workshop on Sensor-based Activity Recognition and Interaction}, abstract = {Experimentation in Wet Laboratories requires tracking and identification of small containers like test tubes, flasks, and bottles. The current practise involves colored adhesive markers, waterproof hand-writing, QR- and Barcodes, or RFID-Tags. These markers are often not self-descriptive and require a lookup table on paper or some digitally stored counterpart. Furthermore they are subject to harsh environmental conditions (e.g. samples are kept in a freezer), and can be hard to share with other lab workers for lack of a consistent annotation systems. Increasing their durability, as well as providing a central tracking system for these containers, is therefore of great interest. In this paper we present a system for the implicit tracking of RFID-augmented containers with a wrist-worn reader unit, and a voice-interaction scheme based on a head-mounted display.}, series = {WOAR '15}, year = {2015}, isbn = {978-1-4503-3454-9}, location = {Rostock, Germany}, pages = {13:1--13:5}, articleno = {13}, numpages = {5}, url = {http://doi.acm.org/10.1145/2790044.2790055}, doi = {10.1145/2790044.2790055}, acmid = {2790055}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {HMD, RFID, google glass, wet laboratory}, } @inproceedings{es_mobilehci2015, editor = {Schneegass, Stefan and Mayer, Sven and Olsson, Thomas and Van Laerhoven, Kristof}, author = {Schneegass, Stefan and Mayer, Sven and Olsson, Thomas and Van Laerhoven, Kristof}, title = {From Mobile to Wearable: Using Wearable Devices to Enrich Mobile Interaction}, booktitle = {Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct}, series = {MobileHCI '15}, acmid = {2795396}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Actuating, Interaction, Sensing, Smart Garments, Wearable Computing, Workshop}, year = {2015}, isbn = {978-1-4503-3653-6}, location = {Copenhagen, Denmark}, pages = {936--939}, doi = {10.1145/2786567.2795396}, numpages = {4}, url = {http://doi.acm.org/10.1145/2786567.2795396}, isbn = {978-1-4503-3578-2}, timestamp = {Fri, 11 Sep 2015 14:28:26 +0200}, abstract = {In the last decades, mobile phones have turned into sensor-rich devices that use different built-in sensors such as accelerometers or gyroscopes. The sensors have enriched the interaction possibilities, allowing, for example, gestural interaction. With the prevalence of wearable devices and peripherals, such as fitness bracelets and breast straps, the input and output possibilities can be further extended with both new sensors and actuators. Current applications could benefit from them, and entirely new applications could be designed. The design space for new applications needs to be identified, which will again drive advances in mobile and wearable computing. This workshop sets focus on wearable devices as means to enrich smartphones and their interaction capabilities. We will discuss the new design space and generate ideas of new applications. Furthermore, we will provide sensors and actuators allowing the participants to implement rapid prototypes of their novel application ideas.}, } @inproceedings{es_nca2015, author = {Rafael Bachiller and Nelson Matthys and Javier Del Cid and Wouter Joosen and Danny Hughes and Kristof Van Laerhoven}, title = {{@}migo: A Comprehensive Middleware Solution for Participatory Sensing Applications}, booktitle = {The 14th IEEE International Symposium on Network Computing and Applications}, year = {2015}, bookurl={\url{http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7371416}}, address = {Cambridge, MA, USA}, doi = {10.1109/NCA.2015.26}, isbn = {978-1-5090-1848-2}, location = {Cambridge, MA, USA}, pages = {1-8}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7371695}, abstract = {In the participatory sensing model, humans may serve as opportunistic sensors and flexible actuators while also consuming sensing services. Integrating humans into sensing systems has the potential to increase scale and reduce costs. However, contemporary participatory sensing software provides poor consideration of user dynamism, which includes: mobility across networks, mobility across devices and context-awareness. To address these limitations we propose the User Component and User Bindings. The former represents the user as a first class reconfigurable element of evolving and shared participatory sensing platforms. The latter allows the middleware to support multiple communications channels including Online Social Networks (OSN) to connect users with sensing applications. Our approach increases user participation, reduces out-of-context interactions and only consumes a limited amount of energy by sharing context information between applications. We support these claims by evaluating our approach on a two weeks experiment in which three participants take part in three concurrent participatory applications.}, } @article{es_phil2015, author = {Dietrich, Manuel and Van Laerhoven, Kristof}, title = {An Interdisciplinary Approach on the Mediating Character of Technologies for Recognizing Human Activity}, journal = {Philosophies}, volume = {1}, year = {2015}, number = {1}, pages = {55}, url = {http://www.mdpi.com/2409-9287/1/1/55}, issn = {2409-9287}, abstract = {In this paper, we introduce a research project on investigating the relation of computers and humans in the field of wearable activity recognition. We use an interdisciplinary approach, combining general philosophical assumptions on the mediating character of technology with the current computer science design practice. Wearable activity recognition is about computer systems which automatically detect human actions. Of special relevance for our research project are applications using wearable activity recognition for self-tracking and self-reflection, for instance by tracking personal activity data like sports. We assume that activity recognition is providing a new perspective on human actions; this perspective is mediated by the recognition process, which includes the recognition models and algorithms chosen by the designer, and the visualization to the user. We analyze this mediating character with two concepts which are both based on phenomenological thoughts namely first Peter-Paul Verbeek’s theory on human-technology relations and second the ideas of embodied interaction. Embedded in the concepts is a direction which leads to the role of technical design in how technology mediates. Regarding this direction, we discuss two case studies, both in the possible using practice of self-tracking and the design practice. This paper ends with prospects towards a better design, how the technologies should be designed to support self-reflection in a valuable and responsible way.}, doi = {10.3390/philosophies1010055} } @inproceedings{es_iwoar2015a, author = {Dietrich, Manuel and van Laerhoven, Kristof}, title = {A Typology of Wearable Activity Recognition and Interaction}, booktitle = {Proceedings of the 2Nd International Workshop on Sensor-based Activity Recognition and Interaction}, abstract = {In this paper, we will provide a typology of sensor-based activity recognition and interaction, which we call wearable activity recognition. The typology will focus on a conceptual level regarding the relation between persons and computing systems. Two paradigms, first the activity based seamless and obtrusive interaction and second activity-tracking for reflection, are seen as predominant. The conceptual approach will lead to the key term of this technology research, which is currently underexposed in a wider and conceptual understanding: human action/activity. Modeling human action as a topic for human-computer interaction (HCI) in general exists since its beginning. We will apply two classic theories which are influential in the HCI research to the application of wearable activity recognition. It is both a survey and a critical reflection on these concepts. As a further goal of our approach, we argue for the relevance and the benefits this typology can have. Beside practical consequences, a typology of the human-computer relation and the discussion of the key term activity can be a medium for exchange which other disciplines. Especially when applications become more serious, for example in health care, a typology including a wider mutual understanding can be useful for cooperations with non-technical practitioners e.g. doctors or psychologists.}, series = {WOAR '15}, year = {2015}, isbn = {978-1-4503-3454-9}, location = {Rostock, Germany}, pages = {1:1--1:8}, articleno = {1}, numpages = {8}, url = {http://doi.acm.org/10.1145/2790044.2790048}, doi = {10.1145/2790044.2790048}, acmid = {2790048}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {HCI theory, activity recognition, interaction design, seamless interaction, self-reflection, ubiquitous computing, wearables}, } @ARTICLE{es_fict2015, AUTHOR={Van Laerhoven, Kristof and Borazio, Marko and Burdinski, Jan Hendrik}, TITLE={Wear is Your Mobile? Investigating Phone Carrying and Use Habits with a Wearable Device}, JOURNAL={Frontiers in ICT}, journalurl={\url{http://journal.frontiersin.org/journal/ict}}, abstracturl={\url{http://journal.frontiersin.org/article/10.3389/fict.2015.00010/abstract}}, VOLUME={2}, YEAR={2015}, NUMBER={10}, URL={http://www.frontiersin.org/mobile_and_ubiquitous_computing/10.3389/fict.2015.00010/abstract}, DOI={10.3389/fict.2015.00010}, ISSN={2297-198X} , ABSTRACT={This article explores properties and suitability of mobile and wearable platforms for continuous activity recognition and monitoring. Mobile phones have become generic computing platforms, and even though they might not always be with the user, they are increasingly easy to develop for and have an unmatched variety of on-board sensors. Wearable units in contrast tend to be purpose-built, and require a certain degree of user adaptation, but they are increasingly used to do continuous sensing. We explore the trade-offs for both device types in a study that compares their sensor data and that explicitly examines how often these devices are being worn by the user. To this end, we have recorded a dataset from 51 participants, who were given a wrist-worn sensor and an app to be used on their Smartphone for two weeks continuously, totalling 638 days (or over 15300 hours) of wearable and mobile data. Results confirm findings of previous studies from North America and show that Smartphones are on average being on their user less than 23\% of the time, mostly during working hours. Just as noteworthy is the high variance in Smartphone use (in carrying, interacting with, and charging the phone) among participants.} } @inproceedings {ess_icwsm2015, title = {DUKE: A Solution for Discovering Neighborhood Patterns in Ego Networks}, booktitle = {The 9th International AAAI Conference on Web and Social Media (ICWSM)}, year = {2015}, month = {05/2015}, publisher = {AAAI}, pages = {268--277}, organization = {AAAI}, address = {Oxford, England}, abstract = {Given the rapid growth of social media websites and the ease of aggregating ever-richer social data, an inevitable research question that can be expected to emerge is whether different interaction patterns of individuals and their meaningful interpretation can be captured for social network analysis. In this work, we present a novel solution that discovers occurrences of prototypical ’ego network’ patterns from social media and mobile-phone networks, to provide a data-driven instrument to be used in behavioral sciences for graph interpretations. We analyze nine datasets gathered from social media websites and mobile phones, together with 13 network measures, and three unsupervised clustering algorithms. Further, we use an unsupervised feature similarity technique to reduce redundancy and extract compact features from the data. The reduced feature subsets are then used to discover ego patterns using various clustering techniques. By cluster analysis, we discover that eight distinct ego neighborhood patterns or ego graphs have emerged. This categorization allows concise analysis of users’ data as they change over time. We provide fine-grained analysis for the validity and quality of clustering results. We perform clustering verification based on the following three intuitions: i) analyzing the clustering patterns for the same set of users crawled from three social media networks, ii) associating metadata information with the clusters and evaluating their performance on real networks, iii) studying selected participants over an extended period to analyze their behavior.}, author = {Agha Muhammad and Van Laerhoven, Kristof}, note = {oral, 19\% acceptance rate}, isbn = {978-1-57735-733-9}, url={\url{http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10551/10510}} } @inproceedings {ess_sas2015, title = {Low-power Lessons from Designing a Wearable Logger for Long-term Deployments}, booktitle = {2015 IEEE Sensors Applications Symposium (SAS 2015)}, year = {2015}, publisher = {IEEE Press}, organization = {IEEE}, doi = {10.1109/SAS.2015.7133581}, address = {Zadar, Croatia}, author = {Berlin, Eugen and Martin Zittel and Michael Br{\"a}unlein and Van Laerhoven, Kristof}, abstract = {The advent of a range of wearable products for monitoring one’s healthcare and fitness has pushed decades of research into the market over the past years. These units record motion and detect common physical activities to assist the wearer in monitoring fitness, general state of health, and sleeping trends. Most of the detection algorithms on board of these devices however are closed-source and the devices do not allow the recording of raw inertial data. This paper presents a project that, faced by these limitations of commercial wearable products, set out to create an open-source recording platform for activity recognition research that (1) is sufficiently power-efficient, and (2) remains small and comfortable enough to wear, to be able to record raw inertial data for extended periods of time. We study especially, via high-resolution power profiling, several trade-offs present in the choice for the basic hardware components of our prototype, and contribute with three key design areas that have had a significant impact on our prototype design.}, url = {http://dx.doi.org/10.1109/SAS.2015.7133581}, } @article {ess_jbhi2015, title = {MyHealthAssistant: An Event-driven Middleware for Multiple Medical Applications on a Smartphone-Mediated Body Sensor Network}, journal = {{IEEE} J. Biomedical and Health Informatics}, volume = {19}, number = {2}, pages = {752--760}, year = {2015}, url = {http://dx.doi.org/10.1109/JBHI.2014.2326604}, doi = {10.1109/JBHI.2014.2326604}, author = {Christian Seeger and Van Laerhoven, Kristof and Buchmann, Alejandro}, abstract = {An ever-growing range of wireless sensors for medical monitoring has shown that there is significant interest in monitoring patients in their everyday surroundings. It however remains a challenge to merge information from several wireless sensors and applications are commonly built from scratch. This paper presents a middleware targeted for medical applications on smartphone-like platforms that relies on an event-based design to enable flexible coupling with changing sets of wireless sensor units, while posing only a minor overhead on the resources and battery capacity of the interconnected devices. We illustrate the requirements for such middleware with three different healthcare applications that were deployed with our middleware solution, and characterize the performance with energy consumption, overhead caused for the smartphone, and processing time under real-world circumstances. Results show that with sensing-intensive applications, our solution only minimally impacts the phone's resources, with an added CPU utilization of 3/% and a memory usage under 7 MB. Furthermore, for a minimum message delivery ratio of 99.9\%, up to 12 sensor readings per second are guaranteed to be handled, regardless of the number of applications using our middleware.} } @y{y14,y={2014}} @inproceedings {ess_ichi2014, title = {Towards Benchmarked Sleep Detection with Inertial Wrist-worn Sensing Units}, author = {Borazio, Marko and Berlin, Eugen and K{\"u}c{\"u}kyildiz, Nagihan and Scholl, Philipp M. and Van Laerhoven, Kristof}, booktitle = {ICHI 2014}, year = {2014}, month = {09/2014}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Verona, Italy}, abstract = {The monitoring of sleep by quantifying sleeping time and quality is pivotal in many preventive health care scenarios. A substantial amount of wearable sensing products have been introduced to the market for just this reason, detecting whether the user is either sleeping or awake. Assessing these devices for their accuracy in estimating sleep is a daunting task, as their hardware design tends to be different and many are closed-source systems that have not been clinically tested. In this paper, we present a challenging benchmark dataset from an open source wrist-worn data logger that contains relatively high-frequent (100Hz) 3D inertial data from 42 sleep lab patients, along with their data from clinical polysomnography. We analyse this dataset with two traditional approaches for detecting sleep and wake states and propose a new algorithm specifically for 3D acceleration data, Estimation of Stationary Sleep-segments (ESS). Results show that all three methods generally over-estimate for sleep, with our method performing slightly better (74\% overall mean accuracy) than the traditional activity count-based methods.}, doi = {10.1109/ICHI.2014.24} } @inproceedings {ess_iacap2014, title = {How does Wearable Activity Recognition Influence Users' Actions? A Computer Science and Philosophy Interdisciplinary Investigation}, booktitle = {IACAP 2014}, year = {2014}, month = {08/2014}, publisher = {Springer}, organization = {Springer}, address = {Thessaloniki, Greece}, author = {Dietrich, Manuel and Van Laerhoven, Kristof} } @inproceedings {ess_gec2014a, title = {Comparing Google Glass with Tablet-PC as Guidance System for Assembling Tasks}, booktitle = {Glass & Eyewear Computers (GEC)}, bookurl = {\url{http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6968673}}, year = {2014}, month = {06/2014}, abstract={Head mounted displays (HMDs) can be used as an guidance system for manual assembling tasks: contrary to using a Tablet-PC, instructions are always shown in the field of view while hands are kept free for the task. This is believed to be one of the major advantage of using HMDs. In the study reported here, performance, visual fatigue, and subjective strain was measured in a dual task paradigm. Participants were asked to follow a toy car assembly instructions while monitoring a virtual gauge. Both tasks had to be executed in parallel either while wearing Google Glass or using a Tablet-PC. Results show slower performance on the HMD but no difference in subjective strain.}, abstracturl={\url{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6970624}}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Zurich, Switzerland}, doi={10.1109/BSN.Workshops.2014.11}, author = {Matthias Wille and Sascha Wischniewski and Philipp M. Scholl and Van Laerhoven, Kristof} } @inproceedings {ess_gec2014b, title = {Diary-Like Long-Term Activity Recognition: Touch or Voice Interaction?}, booktitle = {Glass & Eyewear Computers (GEC)}, bookurl = {\url{http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6968673}}, year = {2014}, abstract={The experience sampling methodology is a well known tool in psychology to asses a subject's condition. Regularly or whenever an important event happens the subject stops whatever he is currently involved in and jots down his current perceptions, experience, and activities, which in turn form the basis of these diary studies. Such methods are also widely in use for gathering labelled data for wearable long-term activity recognition, where subjects are asked to note conducted activities. We present the design of a personal electronic diary for daily activities, including user interfaces on a PC, Smartphone, and Google Glass. A 23-participant structured in-field study covering seven different activities highlights the difference of mobile touch interaction and ubiquitous voice recognition for tracking activities.}, abstracturl={\url{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6970625}}, month = {06/2014}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Zurich, Switzerland}, doi = {10.1109/BSN.Workshops.2014.12}, author = {Philipp M. Scholl, and Borazio, Marko and Martin J{\"a}nsch and Van Laerhoven, Kristof} } @inproceedings{ess_ubicompadj2014, author = {Scholl, Philipp M. and Van Laerhoven, Kristof}, title = {Wearable Digitization of Life Science Experiments}, booktitle = {Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication}, series = {UbiComp '14 Adjunct}, year = {2014}, isbn = {978-1-4503-3047-3}, location = {Seattle, Washington}, pages = {1381--1388}, numpages = {8}, url = {http://doi.acm.org/10.1145/2638728.2641719}, doi = {10.1145/2638728.2641719}, acmid = {2641719}, publisher = {ACM}, abstract = {Experimental work in Life Sciences is done with protective garment to contain harmful agents and to avoid contaminations. This limits the amount of documentation that can be done during experimentation, since pen'n'paper and other equipment is hardly allowed in those environments. Relying on her memory, the scientist has to reconstruct the important details of her experiment later on. Wearable computers, like Google Glass or wrist-worn Smartwatches, can enhance the scientist's ability to record key information while conducting his experiment. Especially the possibility of hands-free, and implicit interaction with the wearable system creates new possibilities for augmenting the scientist's memory.}, address = {New York, NY, USA}, keywords = {RFID, assistance, documentation, google glass, life science, wearable computing}, } @article{ess_crsn2014, author={Scholl, Philipp M. and Brachmann, Martina and Santini, Silvia and Van Laerhoven, Kristof}, editor={Koubaa, Anis and Khelil, Abdelmajid}, title="Integrating Wireless Sensor Nodes in the Robot Operating System", bookTitle="Cooperative Robots and Sensor Networks 2014", year="2014", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="141--157", abstract={The Robot Operating System (ROS) is a popular middleware that eases the design, implementation, and maintenance of robot systems. In particular, ROS enables the integration of a large number of heterogeneous devices in a single system. To allow these devices to communicate and cooperate, ROS requires device-specific interfaces to be available. This restricts the number of devices that can effectively be integrated in a ROS-based system. In this work we present the design, implementation, and evaluation of a ROS middleware client that allows to integrate constrained devices like wireless sensor nodes in a ROS-based system. Wireless sensor nodes embedded in the environment in which a robot system is operating can indeed help robots in navigating and interacting with the environment. The client has been implemented for devices running the Contiki operating system but its design can be readily extended to other systems like, e.g., TinyOS. Our evaluation shows that: in-buffer processing of ROS messages without relying on dynamic memory allocation is possible; message contents can be accessed conveniently using well-known concepts of the C language (structs) with negligible processing overhead with respect to a C++-based client; and that ROS' message-passing abstraction facilitates the integration of devices running event-based systems like Contiki.}, isbn={978-3-642-55029-4}, doi={10.1007/978-3-642-55029-4_7}, url="http://dx.doi.org/10.1007/978-3-642-55029-4_7" } @article {ess_iswc2014, title = {ISWC 2013-Wearables Are Here to Stay.}, journal = {IEEE Pervasive Computing}, volume = {13}, number = {1}, year = {2014}, month = {02/2014}, pages = {14-18}, journalurl={\url{http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7756}}, abstracturl={\url{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6750483}}, abstract = {The 17th edition of the IEEE International Symposium on Wearable Computers (ISWC) was a tremendous success. With 17 years of history, it was the perfect place to get a sense of what has been achieved, where our community stands today, and what lies ahead.}, doi = {10.1109/MPRV.2014.11}, author = {Daniel Roggen and Daniel Gatica-Perez and Masaaki Fukumoto and Van Laerhoven, Kristof} } @y{y13,y={2013}} @inproceedings {ess_sensys2013, title = {A Site Properties Assessment Framework for Wireless Sensor Networks}, booktitle = {The 11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013)}, year = {2013}, month = {12/2013}, publisher = {ACM Press}, organization = {ACM Press}, address = {Rome, Italy}, abstract = {Comparing experimental results obtained on different wireless sensor network (WSN) deployments is typically very cumbersome and in most cases unfeasible. This is due to the lack of a methodology to describe the properties of WSN deployments and the experimental conditions under which experiments have been run. Our work focuses on the design and development of a site properties assessment framework, called SiteWork, that aims at providing a mean to quickly, automatically and accurately quantifying the proper- ties of a WSN. This poster abstract describes the preliminary design and evaluation of the basic site properties assessment mechanisms provided by SiteWork.}, author = {Gurov, Iliya and Guerrero, Pablo and Brachmann, Martina and Silvia Santini and Van Laerhoven, Kristof and Buchmann, Alejandro}, doi = {10.1145/2517351.2517400} } @inproceedings {ess_mum2013, title = {Using Time Use with Mobile Sensor Data: A Road to Practical Mobile Activity Recognition?}, booktitle = {12th International Conference on Mobile and Ubiquitous Multimedia}, year = {2013}, month = {12/2013}, publisher = {ACM Press}, organization = {ACM Press}, address = {Lulea, Sweden}, abstract = {Having mobile devices that are capable of finding out what activity the user is doing, has been suggested as an attractive way to alleviate interaction with these platforms, and has been identified as a promising instrument in for instance medical monitoring. Although results of preliminary studies are promising, researchers tend to use high sampling rates in order to obtain adequate recognition rates with a variety of sensors. What is not fully examined yet, are ways to integrate into this the information that does not come from sensors, but lies in vast data bases such as time use surveys. We examine using such statistical information combined with mobile acceleration data to determine 11 activities. We show how sensor and time survey information can be merged, and we evaluate our approach on continuous day and-night activity data from 17 different users over 14 days each, resulting in a data set of 228 days. We conclude with a series of observations, including the types of activities for which the use of statistical data has particular benefits}, doi = {http://dx.doi.org/10.1145/2541831.2541850}, author = {Borazio, Marko and Van Laerhoven, Kristof} } @inproceedings {ess_ichi2013, title = {A Publish/Subscribe Middleware for Body and Ambient Sensor Networks that Mediates between Sensors and Applications}, booktitle = {IEEE International Conference on Healthcare Informatics (ICHI 2013)}, year = {2013}, note = {best paper award}, month = {09/2013}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Philadelphia, PA}, abstract = {Continuing development of an increasing variety of sensors has led to a vast increase in sensor-based telemedicine solutions. A growing range of modular sensors, and the need of having several applications working with those sensors, has led to an equally extensive increase in efforts for system development. In this paper, we present an event-driven middleware for on-body and ambient sensor networks that allows multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the desired data representation which lifts the burden of adapting the application with respect to the connected sensors off the developers shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the systems adaptation to future applications and sensors at run-time. Application-specific event channels provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation, with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the application-specific event channels and the event transformation with respect to CPU utilization, delivery ratio, and memory usage. }, doi = {10.1109/ICHI.2013.30}, bookurl = {\url{http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6679591}}, author = {Christian Seeger and Van Laerhoven, Kristof and Sauer, Jens and Buchmann, Alejandro} } @inproceedings {ess_r2i2013, title = {When Do You Light a Fire? Capturing Tobacco Use with Situated, Wearable Sensors}, booktitle = { First Workshop on Human Factors and Activity Recognition in Healthcare, Wellness and Assisted Living (Recognize2Interact)}, year = {2013}, month = {09/2013}, publisher = {ACM Press}, organization = {ACM Press}, doi={10.1145/2494091.2497284}, address = {Zurich, Switzerland}, abstract = {An important step towards assessing smoking behavior is to detect and log smoking episodes in an unobtrusive way. Detailed information on an individual{\textquoteright}s consumption can then be used to highlight potential health risks and behavioral statistics to increase the smoker‚Äôs awareness, and might be applied in smoking cessation programs. In this paper, we present an evaluation of two different monitoring prototypes which detect a user{\textquoteright}s smoking behavior, based on augmenting a smoking accessory. Both prototypes capture and record instances when the user smokes, and are sufficiently robust and power efficient to allow deployments of several weeks. A real-world feasibility study with 11 frequently-smoking participants investigates the deployment and adoption of the system, showing that smokers are generally unaware of their daily smoking patterns, and tend to overestimate their consumption. }, author = {Philipp M. Scholl and Nagihan K{\"u}c{\"u}kyildiz and Van Laerhoven, Kristof} } @proceedings {ess_ubiws2013, title = {Bridging the Last Gap: LedTX - Optical Data Transmission of Sensor Data for Web-Services}, year = {2013}, month = {09/2013}, publisher = {ACM Press}, address = {Zurich, Switzerland}, abstract = {Data transmission from small-scale data loggers such as human activity recognition sensors is an inherent system‚Äôs design challenge. Interfaces based on USB or Bluetooth still require platform-dependent code on the retrieval computer system, and therefore require a large maintenance effort. In this paper, we present LedTX, a system that is able to transmit wirelessly through LEDs and the camera included in most user‚Äôs hardware. This system runs completely in modern browsers and presents a uni-directional, platform-independent communication channel. We illustrate this system on the UbiLighter, an instrumented lighter that tracks ones smoking behaviour. }, author = {Philipp M. Scholl and Nagihan K{\"u}c{\"u}kyildiz and Van Laerhoven, Kristof}, doi = {10.1145/2494091.2494104} } @inproceedings {ess_istant2013, title = {Connecting Wireless Sensor Networks to the Robot Operating System}, booktitle = {The 8th International Symposium on Intelligent Systems Techniques for Ad hoc and Wireless Sensor Networks}, year = {2013}, month = {06/2013}, publisher = {Elsevier}, organization = {Elsevier}, address = {Nova Scotia, Canada}, doi = {10.1016/j.procs.2013.06.158}, author = {Philipp M. Scholl and Brahim El Mahjoub and Silvia Santini and Van Laerhoven, Kristof} } @inproceedings {ess_spawc2013, title = {Localization in Wireless Networks via Laser Scanning and Bayesian Compressed Sensing}, booktitle = {IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2013)}, year = {2013}, month = {06/2013}, publisher = {IEEE Press}, organization = {IEEE Press}, doi = {10.1109/SPAWC.2013.6612148}, abstract = {WiFi indoor localization has seen a renaissance with the introduction of RSSI-based approaches. However, manual finger- printing techniques that split the indoor environment into pre-defined grids are implicitly bounding the maximum achievable localization accuracy. WoLF, our proposed Wireless localization and Laser-scanner assisted Fingerprinting system, solves this problem by automating the way indoor fingerprint maps are generated. We furthermore show that WiFi local- ization on the generated high resolution maps can be performed by sparse reconstruction which exploits the peculiarities imposed by the physical characteristics of indoor environments. Particularly, we propose a Bayesian Compressed Sensing (BCS) approach in order to find the position of the mobile user and dynamically determine the sufficient number of APs required for accurate positioning. BCS employs a Bayesian formalism in order to reconstruct a sparse signal using an undetermined system of equations. Experimental results with real data collected in a university building validate WoLF in terms of localization accuracy under actual environ- mental conditions. }, author = {Sofia Nikitaki and Philipp M. Scholl and Van Laerhoven, Kristof and Panagiotis Tsakalides} } @inproceedings {ess_soc2013, title = {Quantitative Analysis of Community Detection Methods for Longitudinal Mobile Data}, booktitle = {International Conference on Social Intelligence and Technology (SOCIETY) 2013}, year = {2013}, month = {05/2013}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {State College, Pennsylvania USA}, abstract = {Mobile phones are now equipped with increasingly large number of built-in sensors that can be utilized to collect long-term socio-temporal data of social interactions. Moreover, the data from different built-in sensors can be combined to predict social interactions. In this paper, we perform quantitative analysis of 6 community detection algorithms to uncover the community structure from the mobile data. We use Bluetooth, WLAN, GPS, and contact data for analysis, where each modality is modelled as an undirected weighted graph. We evaluate community detection algorithms across 6 inter-modality pairs, and use well know partition evaluation features to measure clustering similarity between the pairs. We compare the performance of different methods based on the delivered partitions, and analyse the graphs at different times to find out the community stability. }, isbn = {978-1-4799-0045-9}, doi = {10.1109/SOCIETY.2013.17}, author = {Agha Muhammad and Van Laerhoven, Kristof} } @inproceedings {ess_bsn2013, title = {Allowing Early Inspection of Activity Data from a Highly Distributed Bodynet with a Hierarchical-Clustering-of-Segments Approach}, booktitle = {10th Annual Body Sensor Networks Conference 2013}, year = {2013}, month = {05/2013}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Cambridge, MA}, doi = {10.1109/BSN.2013.6575519}, abstract = {The output delivered by body-wide inertial sensing systems has proven to contain sufficient information to distinguish between a large number of complex physical activities. The bottlenecks in these systems are in particular the parts of such systems that calculate and select features, as the high dimensionality of the raw sensor signals with the large set of possible features tends to increase rapidly. This paper presents a novel method using a hierarchical clustering method on raw trajectory and angular segments from inertial data to detect and analyze the data from such a distributed set of inertial sensors. We illustrate on a public dataset, how this novel way of modeling can be of assistance in the process of designing a fitting activity recognition system. We show that our method is capable of highlighting class-representative modalities in such high-dimensional data and can be applied to pinpoint target classes that might be problematic to classify at an early stage. }, author = {Kreil, Matthias and Van Laerhoven, Kristof and Lukowicz, Paul} } @inproceedings {ess_dcoss2013, title = {Sensor Networks for Railway Monitoring: Detecting Trains from their Distributed Vibration Footprints}, booktitle = {9th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 2013)}, year = {2013}, month = {05/2013}, pages = {80-87}, publisher = {IEEE Press}, organization = {IEEE Press}, doi={10.1109/DCOSS.2013.38}, address = {Cambridge, MA, USA}, abstract = {We report in this paper on a wireless sensor network deployment at railway tracks to monitor and analyze the vibration patterns caused by trains passing by. We investigate in particular a system that relies on having a distributed network of nodes that individually contain efficient feature extraction algorithms and classifiers that fit the restricted hardware resources, rather than using few complex and specialized sensors. A feasibility study is described on the raw data obtained from a real-world deployment on one of Europe{\textquoteright}s busiest railroad sections, which was annotated with the help of video footage and contains vibration patterns of 186 trains. These trains were classified in 6 types by various methods, the best performing at an accuracy of 97\%. The trains{\textquoteright} length in wagons was estimated with a mean-squared error of 3.98. Visual inspection of the data shows further opportunities in the estimation of train speed and detection of worn-down cargo wheels. }, author = {Berlin, Eugen and Van Laerhoven, Kristof} } @inproceedings {ess_netmob2013a, title = {Discovering Common Structures in Mobile Call Data: An Efficient Way to Clustering Ego Graphs}, booktitle = {Third conference on the Analysis of Mobile Phone Datasets (NetMob2013), Special session on the D4D challenge}, year = {2013}, month = {05/2013}, address = {Cambridge/MA}, abstract = {This paper conducts a study on a mobile call data set from 5000 individuals in order to examine what type of prototypical calling behaviors tend to occur for individual users. By representing the call data with methods from graph analysis, several features are suggested to characterize the shape and type of neighborhood graph around each mobile phone user. By cluster analysis of these features for all mobile users, we show that the data set contains seven distinct types of so-called neighborhood graphs or ego graphs. This categorization allows concise analysis of users‚Äô call data as they change over time and might be used as a sociological tool to detect rhythms and outliers in call behavior. }, author = {Agha Muhammad and Van Laerhoven, Kristof} } @inproceedings {ess_netmob2013b, title = {Quantitative Analysis of Community Detection Methods for Longitudinal Mobile Data}, booktitle = {Third conference on the Analysis of Mobile Phone Datasets (NetMob2013)}, year = {2013}, month = {05/2013}, address = {Cambridge/MA}, doi={10.1109/SOCIETY.2013.17}, abstract = {Mobile phones are now equipped with an increasingly large number of built-in sensors, that can be utilized to collect long-term socio-temporal data of social interactions. Moreover, the data from different built-in sensors can be combined to predict social interactions. In this paper, we perform quantitative analysis of 6 famous community detection algorithms to uncover the community structure from the mobile data. We use Bluetooth, WLAN, GPS, and contact data for analysis, where each modality is modelled as an undirected weighted graph. We evaluate community detection algorithms across 6 intermodality pairs, and use well-know partition evaluation features to measure clustering similarity between the pairs. We compare the performance of different methods based on the delivered partitions.}, author = {Agha Muhammad and Van Laerhoven, Kristof} } @inproceedings {ess_ah2013, title = {Improving Activity Recognition without Sensor Data: A Comparison Study of Time Use Surveys}, booktitle = {4th International Augmented Human Conference }, year = {2013}, month = {03/2013}, publisher = {ACM Press}, organization = {ACM Press}, address = {Stuttgart, Germany}, doi={10.1145/2459236.2459255}, abstract = {Wearable sensing systems, through their proximity with their user, can be used to automatically infer the wearer{\textquoteright}s activity to obtain detailed information on availability, behavioural patterns, and health. For this purpose, classifiers need to be designed and evaluated with sufficient training data from these sensors and from a representative set of users, which requires starting this procedure from scratch for every new sensing system and set of activities. To alleviate this procedure and optimize classification performance, the use of time use surveys has been suggested: These large databases contain typically several days worth of detailed activity information from a large population of hundreds of thousands of participants. This paper uses a strategy first suggested by Partridge in 2008 that utilizes time use diaries in an activity recognition method. We offer a comparison of the aforementioned North-American data with a large European database, showing that although there are several cultural differences, certain important features are shared between both regions. By cross-validating across the 5160 households in this new data with activity data of 13798 individuals, especially distinctive features turn out to be time and participant{\textquoteright}s location. }, author = {Borazio, Marko and Van Laerhoven, Kristof} } @article {ess_ijhci2013, title = {Already Up? Using Mobile Phones to Track & Share Sleep Behavior}, journal = {International Journal of Human-Computer Studies}, year = {2013}, publisher = {Elsevier}, abstract = {Users share a lot of personal information with friends, family members, and colleagues via social networks. Surprisingly, some users choose to share their sleeping patterns, perhaps both for awareness as well as a sense of connection to others. Indeed, sharing basic sleep data, whether a person has gone to bed or waking up, informs others about not just one‚Äôs sleeping routines but also indicates physical state, and reflects a sense of wellness. We present Somnometer, a social alarm clock for mobile phones that helps users to capture and share their sleep patterns. While the sleep rating is obtained from explicit user input, the sleep duration is estimated based on monitoring a user‚Äôs interactions with the app. Observing that many individuals currently utilize their mobile phone as an alarm clock revealed behavioral patterns that we were able to leverage when designing the app. We assess whether it is possible to reliably monitor one‚Äôs sleep duration using such apps. We further investigate whether providing users with the ability to track their sleep behavior over a long time period can empower them to engage in healthier sleep habits. We hypothesize that sharing sleep information with social networks impacts awareness and connectedness among friends. The result from a controlled study reveals that it is feasible to monitor a user‚Äôs sleep duration based just on her interactions with an alarm clock app on the mobile phone. The results from both an in-the-wild study and a controlled experiment suggest that providing a way for users to track their sleep behaviors increased user awareness of sleep patterns and induced healthier habits. However, we also found that, given the current broadcast nature of existing social networks, users were concerned with sharing their sleep patterns indiscriminately. }, issn = {1071-5819}, doi = {10.1016/j.ijhcs.2013.03.001}, author = {Shirazi, Alireza Sahami and James Clawson and Yashar Hassanpour and Mohammad J. Tourian and Ed Chi and Borazio, Marko and Schmidt, Albrecht and Van Laerhoven, Kristof} } @y{y12,y={2012}} @inproceedings {ess_senseapp2012, title = {Diagnosing the Weakest Link in WSN Testbeds: A Reliability and Cost Analysis of the USB Backchannel}, booktitle = {7th IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2012)}, year = {2012}, note = {oral, 20\% acceptance rate}, month = {10/2012}, publisher = {IEEE Press}, organization = {IEEE Press}, doi={10.1109/LCNW.2012.6424085}, address = {Clearwater Beach, FL, USA}, abstract = {This paper highlights and characterizes the main obstacle to deploying a robust wireless sensor network testbed: the USB connections that link each of the nodes via ethernet gateways to the central server. Unfortunately, these connections are also the components that, when properly installed, can reduce testbed costs by attaching multiple nodes per gateway. After illustrating how unreliable current solutions can become (regardless of the used sensor nodes, USB cabling, or gateway setup), a set of experiments led to a list of dos and don{\textquoteright}ts in testbed deployment. Furthermore, a simple and cost-effective suggestion is presented that allows to bypass current USB backchannel issues, leading to a robuster testbed that avoids manual maintenance of individual nodes. }, author = {Guerrero, Pablo and Gurov, Iliya and Buchmann, Alejandro and Van Laerhoven, Kristof} } @article {ess_pervasive2012, title = {Are You in Bed with Technology?}, journal = {IEEE Pervasive Computing}, volume = {11}, number = {4}, year = {2012}, month = {10/2012}, pages = {4-7}, chapter = {4}, abstract = {Good sleep can positively affect our performance, while a lack of sleep can affect our memory, immune system, cognitive functioning, learning abilities, and alertness. Researchers have thus investigated numerous technologies and approaches to monitor individuals{\textquoteright} sleep behavior at home. One goal of such technologies is to collect information that can increase users{\textquoteright} awareness of their sleep habits to persuade them to adopt healthier routines. This department looks at new products in this space, ranging from smart alarm clocks to advanced monitoring devices. }, issn = {1536-1268}, doi = {10.1109/MPRV.2012.63}, author = {Schmidt, Albrecht and Shirazi, Alireza Sahami and Van Laerhoven, Kristof} } @inproceedings {ess_ubicomp2012, title = {Detecting Leisure Activities with Dense Motif Discovery}, booktitle = {14th ACM International Conference on Ubiquitous Computing (UbiComp 2012)}, year = {2012}, note = {oral, 19.3\% acceptance rate}, month = {09/2012}, pages = {250-259}, publisher = {ACM Press}, organization = {ACM Press}, address = {Pittsburgh, PA, USA}, abstract = {This paper proposes an activity inference system that has been designed for deployment in mood disorder research, which aims at accurately and efficiently recognizing selected leisure activities in week-long continuous data. The approach to achieve this relies on an unobtrusive and wrist-worn data logger, in combination with a custom data mining tool that performs early data abstraction and dense motif discovery to collect evidence for activities. After presenting the system design, a feasibility study on weeks of continuous inertial data from 6 participants investigates both accuracy and execution speed of each of the abstraction and detection steps. Results show that our method is able to detect target activities in a large data set with a comparable precision and recall to more conventional approaches, in approximately the time it takes to download and visualize the logs from the sensor. }, author = {Berlin, Eugen and Van Laerhoven, Kristof}, doi = {10.1145/2370216.2370257} } @inproceedings {ess_inss2012a, title = {Trainspotting: Combining Fast Features to Enable Detection on Resource-constrained Sensing Devices}, booktitle = {The Ninth International Conference on Networked Sensing Systems (INSS 2012)}, year = {2012}, month = {06/2012}, pages = {1-8}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Antwerp, Belgium}, abstract = {This paper focuses on spotting and classifying complex and sporadic phenomena directly on a sensor node, whereby a relatively long sequence of sensor samples needs to be considered at a time. Using fast feature extraction from streaming data that can be implemented on the sensor nodes, we show that on-sensor event classification can be achieved. This approach is of particular interest for wireless sensor networks as it promises to reduce wireless traffic significantly, as only events need to be transmitted instead of potentially large chunks of inertial data. The presented approach characterizes the essence of an event's signal by combining several simple features on low-cost MEMS inertial data. Using a scenario and real data from vibration signatures generated by passing trains, we show how with this approach the classification and the estimation of length for passing trains is possible on miniature nodes placed near the railroad tracks. Experiments show that, at the cost of slightly more local processing, the chosen features produce good train type classification with up to 90\% of trains correctly identified. }, isbn = {978-1-4673-1784-9}, doi = {10.1109/INSS.2012.6240520}, author = {Berlin, Eugen and Van Laerhoven, Kristof} } @inproceedings {ess_inss2012b, title = {jNode: a Sensor Network Platform that Supports Distributed Inertial Kinematic Monitoring}, booktitle = {The Ninth International Conference on Networked Sensing Systems (INSS 2012)}, year = {2012}, month = {06/2012}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Antwerp, Belgium}, abstract = {Because of the intrinsic advantages of wireless inertial motion tracking, standalone devices that integrate inertial motion units with wireless networking capabilities have gained much interest in recent years. Several platforms, both commercially available and academic, have been proposed to balance the challenges of a small form-factor, power consumption, accuracy and processing speed. Applications include ambulatory monitoring to support healthcare, sport activity analysis, recognizing human group behaviour, navigation support for humans, robots and unmanned vehicles, but also in structural monitoring of large buildings. This paper provides an analysis of the current state-of-the-art platforms in wireless inertial motion tracking and presents a novel hybrid tracking platform that is extensible, low-power, flexible enough to be used for both short- and long-term monitoring and based on a firmware that allows it to be easily adapted after being deployed. After describing the architecture, the design choices in both hardware and software, and arguing why the jNode platform is different from previous work, a performance characterization is given of a fully functional prototype. }, isbn = {978-1-4673-1784-9}, doi={10.1109/INSS.2012.6240553}, author = {Philipp M. Scholl and Matthias Berning and Van Laerhoven, Kristof and Markus Scholz and Dawud Gordon} } @inproceedings {ess_inss2012c, title = {Fast Indoor Radio-Map Building for RSSI-based Localization Systems}, booktitle = {The Ninth International Conference on Networked Sensing Systems (INSS 2012)}, year = {2012}, month = {06/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Antwerp, Belgium}, abstract = {Wireless Indoor localization systems based on RSSI-values typically consist of an offline training phase and online position determination phase. During the offline phase, georeferenced RSSI measurement, called fingerprints, are recorded to build a radiomap of the building. This radiomap is then searched during the position determination phase to estimate another nodes location. Usually the radiomap is build manually, either by users pin-pointing their location on a ready-made floorplan or by moving in pre-specified patterns while scanning the network for RSSI values. This cumbersome process leads to inaccuracies in the radiomap. Here, we propose a system to build the indoor- and radio-map simultaneously by using a handheld mapping system employing a laser scanner in an IEEE802.15.4-compatible network. This makes indoor- and radio-mapping for wireless localization less cumbersome, faster and more reliable. }, isbn = {978-1-4673-1784-9}, doi={10.1109/INSS.2012.6240574}, author = {Philipp M. Scholl and Stefan Kohlbrecher and Vinay Sachidananda and Van Laerhoven, Kristof} } @inproceedings {ess_mdc2012, title = {Discovery of User Groups within Mobile Data}, booktitle = {Nokia Mobile Data Challenge (MDC 2012)}, year = {2012}, month = {06/2012}, address = {Newcastle, UK}, author = {Agha Muhammad and Van Laerhoven, Kristof} } @inproceedings {ess_ewsn2012, title = {TUDuNet, a Metropolitan-Scale Federation of Wireless Sensor Network Testbeds}, booktitle = {9th European Conference on Wireless Sensor Networks (EWSN 2012)}, year = {2012}, month = {02/2012}, address = {Trento, Italy}, abstract = {To address the real-world challenges in sensor network evaluation, testbeds have been proposed to enable experimentation without taking the typical deployment hurdles of robustly mounting the hardware, installing batteries, and instrumenting sensor nodes for data collection. In the recent past, several research institutions across Europe proposed to federate their testbeds. However, providing scalability and transparency despite the high heterogeneity in hardware and software between sites proves to be a tough problem. In this paper we introduce TUDNet, a metropolitan-scale federation of sensor network testbeds that spans several buildings within a city. We describe its architecture, the current sites and the control infrastructure as solution for managing experiments at metropolitan scale.}, author = {Guerrero, Pablo and Buchmann, Alejandro and Khelil, Abdelmajid and Van Laerhoven, Kristof} } @inproceedings {ess_IHI2012b, title = {Combining Wearable and Environmental Sensing into an Unobtrusive Tool for Long-Term Sleep Studies}, booktitle = {2nd ACM SIGHIT International Health Informatics Symposium (IHI 2012)}, year = {2012}, note = {oral, 18\% acceptance rate}, month = {01/2012}, publisher = {ACM Press}, organization = {ACM Press}, address = {Miami, Florida, USA}, abstract = {Long-term sleep monitoring of patients has been identified as a useful tool to observe sleep trends manifest themselves over weeks or months for use in behavioral studies. In practice, this has been limited to coarse-grained methods such as actigraphy, for which the levels of activity are logged, and which provide some insight but have simultaneously been found to lack accuracy to be used for studying sleeping disorders. This paper presents a method to automatically detect the user{\textquoteright}s sleep at home on a long-term basis. Inertial, ambient light, and time data tracked from a wrist-worn sensor, and additional night vision footage is used for later expert inspection. An evaluation on over 4400 hours of data from a focus group of test subjects demonstrates a high re-call night segment detection, obtaining an average of 94\%. Further, a clustering to visualize reoccurring sleep patterns is presented, and a myoclonic twitch detection is introduced, which exhibits a precision of 74\%. The results indicate that long-term sleep pattern detections are feasible. }, isbn = {978-1-4503-0781-9}, doi = {10.1145/2110363.2110375}, author = {Borazio, Marko and Van Laerhoven, Kristof} } @inproceedings {ess_IHI2012, title = {An Event-based BSN Middleware that supports Seamless Switching between Sensor Configurations}, booktitle = {2nd ACM SIGHIT International Health Informatics Symposium (IHI 2012)}, year = {2012}, note = {oral, 18\% acceptance rate}, publisher = {ACM Press}, organization = {ACM Press}, address = {Miami, FL}, abstract = {Recent advances in wearable sensors have surged in novel fitness and preventive health care systems that measure step counts, activity levels, and performed exercises with inertial sensors, enabling users to monitor condition and day-to-day lifestyle. This paper presents a middleware designed for a smartphone unit to support health monitoring applications. Its event-driven architecture enables modular system design and seamless switching between sets of embedded sensors. The strengths of the middleware are highlighted in a deployed feasibility study where daily and gym activities are recognized through an interchangeable set of wireless sensors. The study demonstrates that the setup is suitable for daily usage with minimal impact on the phone{\textquoteright}s resources. }, isbn = {978-1-4503-0781-9}, doi = {10.1145/2110363.2110420}, author = {Christian Seeger and Buchmann, Alejandro and Van Laerhoven, Kristof} } @inproceedings {ess_esiot2012, title = {A Feasibility Study of Wrist-Worn Accelerometer Based Detection of Smoking Habits}, booktitle = {esIoT 2012}, year = {2012}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Palermo, Italy}, doi={10.1109/IMIS.2012.96}, abstract = {Cigarette smoking is one of the major causes of lung cancer, and has been linked to a large amount of other cancer types and diseases. Smoking cessation, the only mean to avoid these serious risks, is hindered by the ease to ignore these risks in day-to-day life. In this paper we present a feasibility study with smokers wearing an accelerometer device on their wrist over the course of a week to detect their smoking habits based on detecting typical gestures carried out while smoking a cigarette. We provide a basic detection method that identifies when the user is smoking, with the goal of building a system that provides an individualized risk estimation to increase awareness and motivate smoke cessation. Our basic method detects a typical smoking gestures with a precision of 51.2\% and a user specific recall of over 70\% - creating evidence that an unobtrusive wrist- worn sensor can detect smoking. }, author = {Philipp M. Scholl and Van Laerhoven, Kristof} } @book {ess_ambient2012, title = {Constructing Ambient Intelligence}, series = {Communications in Computer and Information Science}, volume = {277}, year = {2012}, pages = {235}, publisher = {Springer Verlag}, organization = {Springer Verlag}, doi={10.1007/978-3-642-31479-7}, address = {Heidelberg}, abstract = {This book constitutes the refereed proceedings of the AmI 2011 Workshops, held in Amsterdam, The Netherlands, in November 2011. The 55 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on aesthetic intelligence: designing smart and beautiful architectural spaces; ambient intelligence in future lighting systems; interactive human behavior analysis in open or public spaces; user interaction methods for elderly, people with dementia; empowering and integrating senior citizens with virtual coaching; integration of AMI and AAL platforms in the future internet (FI) platform initiative; ambient gaming; human behavior understanding: inducing behavioral change; privacy, trust and interaction in the internet of things; doctoral colloquium.}, isbn = {978-3-642-31478-0}, author = {Wichert, Reiner and Van Laerhoven, Kristof and Gelissen, Jean} } @inproceedings {ess_green2012, title = {A Metropolitan-Scale Testbed for Heterogeneous Wireless Sensor Networks to Support CO2 Reduction}, booktitle = {The Second International Conference on Green Communications and Networking}, year = {2012}, address = {Spain}, doi={10.1007/978-3-642-37977-2_11}, abstract = {There exist two major contributions of network technology to reduce CO2 levels: reducing the energy consumption of the network itself, and supporting areas of application to reduce CO2 levels. The impact of the latter is potentially higher. Therefore, we present TUDŒºNet, a testbed for a metropolitan-scale heterogeneous sensor network with hundreds of nodes that help monitor and control CO2 levels in urban areas. Our testbed has four major application domains where it is being applied: TU Darmstadt{\textquoteright}s award winning solar house, where temperature and CO2 levels are monitored; an 80 year old building in which a WSN is deployed to measure ambient parameters that contribute to future energy-saving remodeling; mobile sensors mounted on the streetcars of the public tramway system to measure location-specific CO2 levels that are collected in a publicly accessible database to obtain CO2 profiles; and a hybrid sensor network in TUD{\textquoteright}s botanical garden to measure humidity, CO2 levels and soil properties to improve the management of urban parks. In this paper we present the concepts behind the design of our testbed, its design challenges and our solutions, and potential applications of such metropolitan-scale sensor networks.}, author = {Guerrero, Pablo and M{\"u}hlh{\"a}user, Max and Strufe, Thorsten and Schneckenburger, Stefan and Hegger, Manfred and Kretzschmar, Birgitt and Buchmann, Alejandro and Van Laerhoven, Kristof and Schweizer, Immanuel} } @y{y11,y={2011}} @inproceedings {ess_MidSens2011, title = {Wireless Sensor Networks in the Wild: Three Practical Issues after a Middleware Deployment}, booktitle = {the Sixth International Workshop on Middleware Tools, Services and Run-time Support for Networked Embedded Systems (MidSens 2011)}, year = {2011}, month = {12/2011}, publisher = {ACM Press}, organization = {ACM Press}, address = {Lisbon, Portugal}, abstract = {This paper reflects on experiences in deploying middleware for a body sensor network, using commercial biosensors. Three types of issues are highlighted that arose during the deployment, which impact middleware design in particular: 1) How can the architecture cope with different levels of data fidelity and propagate those levels to the applications? 2) What is the optimal way to handle temporary disconnections from sensors? and 3) How should the middleware implement sensor-specific peculiarities? Although these issues are described using a specific and demanding health care scenario, we argue that the underlying causes tend to be archetypal for a generic set of sensor network middleware. Awareness of these problem categories and possible solutions are therefore generally relevant for other researchers working on middleware designs for all kinds of sensor networks.}, isbn = {978-1-4503-1069-7}, doi = {10.1145/2090296.2090297}, author = {Christian Seeger and Buchmann, Alejandro and Van Laerhoven, Kristof} } @inproceedings {ess_bodynets2011, title = {myHealthAssistant: A Phone-based Body Sensor Network that Captures the Wearer{\textquoteright}s Exercises throughout the Day}, booktitle = {The 6th International Conference on Body Area Networks}, year = {2011}, note = {Best Paper Award}, month = {11/2011}, publisher = {ACM Press}, organization = {ACM Press}, address = {Beijing, China }, isbn = {978-1-936968-29-9}, abstract = {This paper presents a novel fitness and preventive health¬†care system with a flexible and easy to deploy platform.¬†By using embedded wearable sensors in combination with a¬†smartphone as an aggregator, both daily activities as well as specific gym exercises and their counts are recognized and¬†logged. The detection is achieved with minimal impact on¬†the system{\textquoteright}s resources through the use of customized 3D inertial sensors embedded in fitness accessories with built-in¬†pre-processing of the initial 100Hz data. It provides a flexible re-training of the classifiers on the phone which allows deploying the system swiftly. A set of evaluations shows a classification performance that is comparable to that of state of the art activity recognition, and that the whole setup is suitable for daily usage with minimal impact on the phone's resources.}, author = {Christian Seeger and Buchmann, Alejandro and Van Laerhoven, Kristof} } @inbook {ess_interhub2011, title = {A Feature Set Evaluation for Activity Recognition with Body-Worn Inertial Sensors}, booktitle = {Constructing Ambient Intelligence }, volume = {277}, year = {2011}, month = {11/2011}, pages = {101-109}, publisher = {Springer Verlag}, organization = {Springer Verlag}, chapter = {3}, address = {Amsterdam}, abstract = {The automatic and unobtrusive identification of user activities is a challenging goal in human behavior analysis. The physical activity that a user exhibits can be used as contextual data, which can inform applications that reside in public spaces. In this paper, we focus on wearable inertial sensors to recognize physical activities. Feature set evaluation for 5 typical activities is performed by measuring accuracy for combinations of 6 often-used features on a set of 11 well-known classifiers. To verify significance of this analysis, a t-test evaluation was performed for every combination of these feature subsets. We identify an easy-to-compute feature set, which has given us significant results and at the same time utilizes a minimum of resources }, issn = {978-3-642-31478-0}, doi = {10.1007/978-3-642-31479-7_17}, author = {Agha Muhammad and Niklas Klein and Van Laerhoven, Kristof and Klaus David} } @inproceedings {ess_ami2011, title = {Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data}, booktitle = {International Joint Conference on Ambient Intelligence (AmI-11)}, volume = {LNCS 7040}, year = {2011}, month = {11/2011}, pages = {151{\textendash}156}, publisher = {Springer Verlag}, organization = {Springer Verlag}, address = {Amsterdam}, abstract = {This paper conducts a preliminary study in which sleeping behavior is predicted using long-term activity data from a wearable sensor. For this purpose, two scenarios are scrutinized: The first predicts sleeping behavior using a day-of-the-week model. In a second scenario typical sleep patterns for either working or weekend days are modeled. In a continuous experiment over 141 days (6 months), sleeping behavior is characterized by four main features: the amount of motion detected by the sensor during sleep, the duration of sleep, and the falling asleep and waking up times. Prediction of these values can be used in behavioral sleep analysis and beyond, as a component in healthcare systems. }, isbn = {978-3-642-25166-5}, doi = {10.1007/978-3-642-25167-2_18}, author = {Borazio, Marko and Van Laerhoven, Kristof} } @inproceedings {ess_bodynets2011b, title = {Poster Abstract: Adaptive Gym Exercise Counting for myHealthAssistant}, booktitle = {Body Area Networks (BodyNets)}, year = {2011}, month = {11/2011}, publisher = {ACM Press}, organization = {ACM Press}, address = {Beijing, China}, doi = {10.4108/icst.bodynets.2011.247201}, author = {Christian Seeger and Buchmann, Alejandro and Van Laerhoven, Kristof} } @article {ess_pc2011, title = {ISWC 2010: The Latest in Wearable Computing Research}, journal = {IEEE Pervasive Computing}, volume = {10}, number = {1}, year = {2011}, month = {01/2011}, publisher = {IEEE Press}, chapter = {8}, address = {New York}, journalurl={\url{http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7756}}, abstract = {At the 14th IEEE International Symposium on Wearable Computers (ISWC 2010), researchers and practitioners from all corners of the world met in Seoul‚Äôs impressive Coex Center to review recent advances in e-textiles, on-body sensors, and wearable activity-recognition and mobile technologies. The rich program followed the conference‚Äôs tradition of a technical program consisting of long and short papers, posters, and demonstrations, flanked by three tutorials, a design contest, and a PhD forum. The technical program reflected aspects of wearable computing ranging from fabric-based integrated-circuit design to novel microvibration activity sensors. }, issn = {1536-1268}, doi = {10.1109/MPRV.2011.5}, author = {Van Laerhoven, Kristof} } @y{y10,y={2010}} @proceedings {ess_eurossc2010, title = {How to Log Sleeping Trends? A Case Study on the Long-Term Capturing of User Data}, volume = {Proceedings of EuroSSC 2010, LNCS 6446}, year = {2010}, month = {2010}, pages = {15-27}, publisher = {Springer Verlag}, address = {Passau, Germany}, abstract = {Designing and installing long-term monitoring equipment in the users‚Äô home sphere often presents challenges in terms of reliability, privacy, and deployment. Taking the logging of sleeping postures as an example, this study examines data from two very different modalities, high-fidelity video footage and logged wrist acceleration, that were chosen for their ease of setting up and deployability for a sustained period. An analysis shows the deployment challenges of both, as well as what can be achieved in terms of detection accuracy and privacy. Finally, we evaluate the benefits that a combination of both modalities would bring. }, doi = {10.1007/978-3-642-16982-3_2}, author = {Holger Becker and Borazio, Marko and Van Laerhoven, Kristof} } @proceedings {ess_iswc2010, title = {Proceedings of the 14th International Symposium on Wearable Computers (ISWC 2010)}, year = {2010}, month = {12/2010}, publisher = {IEEE Press}, address = {Seoul, South Korea}, isbn = {978-1-4244-9046-2}, doi = {10.1109/ISWC.2010.5665889}, author = {Kyu-ho Park and Hoi-jun Yoo and Van Laerhoven, Kristof} } @inbook {ess_buchmann2010, title = {Towards Declarative Query Scoping in Sensor Networks}, booktitle = {From Active Data Management to Event-Based Systems and More}, series = {Lecture Notes in Computer Science}, volume = {6462}, year = {2010}, month = {11/2010}, pages = {281-292}, publisher = {Springer Verlag}, organization = {Springer Verlag}, doi={10.1007/978-3-642-17226-7_17}, isbn = {978-3-642-17225-0}, author = {Jacobi, Daniel and Pablo Ezequiel Guerrero and Khalid Nawaz and Christian Seeger and Herzog, Arthur and Van Laerhoven, Kristof and Ilia Petrov} } @inproceedings {ess_senseapp2010, title = {An On-Line Piecewise Linear Approximation Technique for Wireless Sensor Networks}, booktitle = {5th IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2010)}, year = {2010}, month = {10/2010}, pages = {921-928}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Denver, Colorado, USA}, abstract = {Many sensor network applications observe trends over an area by regularly sampling slow-moving values such as humidity or air pressure (for example in habitat monitoring). Another well-published type of application aims at spotting sporadic events, such as sudden rises in temperature or the presence of methane, which are tackled by detection on the individual nodes. This paper focuses on a zone between these two types of applications, where phenomena that cannot be detected on the nodes need to be observed by relatively long sequences of sensor samples. An algorithm that stems from data mining is proposed that abstracts the raw sensor data on the node into smaller packet sizes, thereby minimizing the network traffic and keeping the essence of the information embedded in the data. Experiments show that, at the cost of slightly more processing power on the node, our algorithm performs a shape abstraction of the sensed time series which, depending on the nature of the data, can extensively reduce network traffic and nodes{\textquoteright} power consumption. }, doi = {10.1109/LCN.2010.5735832}, author = {Berlin, Eugen and Van Laerhoven, Kristof} } @inproceedings {ess_iswc2010, title = {Characterizing Sleeping Trends from Postures}, booktitle = {Proceedings of the 14th IEEE International Symposium on Wearable Computers (ISWC 2010)}, year = {2010}, note = {21\% acceptance rate}, month = {10/2010}, pages = {167-168}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Seoul, South Korea}, abstract = {We present an approach to model sleeping trends, using a light-weight setup to be deployed over longer time-spans and with a minimum of maintenance by the user. Instead of characterizing sleep with traditional signals such as EEG and EMG, we propose to use sensor data that is a lot weaker, but also less invasive and that can be deployed unobtrusively for longer periods. By recording wrist-worn accelerometer data during a 4-week-long study, we explore in this poster how sleeping trends can be characterized over long periods of time by using sleeping postures only. }, isbn = {978-1-4244-9046-2}, doi = {10.1109/ISWC.2010.5665853}, author = {Borazio, Marko and Blanke, Ulf and Van Laerhoven, Kristof} } @inproceedings {ess_actrec2010, title = {Standing on the Shoulders of Other Researchers - A Position Statement}, booktitle = {Proc. of the Workshop "How to do good activity recognition research? Experimental methodologies, evaluation metrics, and reproducibility issues" (Pervasive 2010)}, year = {2010}, month = {05/2010}, address = {Helsinki, Finland}, abstract = {Activity Recognition has made significant progress in the past years. We strongly believe however that we could make far greater progress if we build more systematically on each other‚Äôs work. Comparing the activity recognition community with other more mature communities (e.g., those of computer vision and speech recognition) there appear to be two key- ingredients that are missing in ours. First, the more mature communities have established a set of well-defined or accepted research problems, and second, the communities have a tradition to compare their algorithms on established and shared benchmark datasets. Establishing both of these ingredients and evolving them over time in a more explicit manner should enable us to progress our field more rapidly. }, author = {Blanke, Ulf and Larlus, Diane and Van Laerhoven, Kristof and Schiele, Bernt} } @article {ess_IPSJ2010, title = {A Method for Context Recognition Using Peak Values of Sensors}, journal = {Transactions of Information Processing Society of Japan}, volume = {51}, number = {3}, year = {2010}, month = {03/2010}, pages = {1068-1077}, publisher = {Information Processing Society of Japan (IPSJ)}, issn = {03875806}, URL={http://ci.nii.ac.jp/naid/110007970709/en/}, author = {Kazuya Murao and Van Laerhoven, Kristof and Tsutomu Terada and Shojiro Nishio} } @inproceedings {ess_tei2010, title = {Coming to Grips with the Objects We Grasp: Detecting Interactions with Efficient Wrist-Worn Sensors}, booktitle = {International Conference on Tangible and Embedded Interaction (TEI 2010)}, year = {2010}, month = {01/2010}, pages = {57-64}, publisher = {ACM Press}, organization = {ACM Press}, address = {Cambridge MA, USA}, isbn = {978-1-60558-841-4}, doi = {10.1145/1709886.1709898}, author = {Berlin, Eugen and Liu, Jun and Van Laerhoven, Kristof and Schiele, Bernt} } @y{y99,y={1999-2009}} @inproceedings {mis_sensys2009, title = {Demo Abstract: Whac-A-Bee -- A Sensor Network Game}, booktitle = {Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (SenSys 2009)}, year = {2009}, month = {11/2009}, pages = {333-334}, publisher = {ACM Press}, organization = {ACM Press}, address = {Berkeley, California, USA}, abstract = {This paper illustrates both challenges and benefits found in expanding a traditional game concept to a situated environment with a distributed set of wireless sensing modules. Our pervasive game equivalent of the Whac-A-Mole game, Whac-A-Bee, retains the find-and-seek aspects of the original game while extending the location, the number of players, and the time-span in which it can be played. We discuss the obstacles met during this work, and specifically address challenges in making the game robust and flexible enough for large and long-term deployments in unknown territory. }, isbn = {978-1-60558-519-2}, doi = {10.1145/1644038.1644087}, author = {Berlin, Eugen and Guerrero, Pablo and Herzog, Arthur and Jacobi, Daniel and Van Laerhoven, Kristof and Schiele, Bernt and Buchmann, Alejandro} } @inproceedings {mis_iswc2009, title = {When Else Did This Happen? Efficient Subsequence Representation and Matching for Wearable Activity Data}, booktitle = {Proceedings of the 13th International Symposium on Wearable Computers (ISWC 2009)}, year = {2009}, pages = {69-77}, publisher = {IEEE Press}, organization = {IEEE Press}, doi = {10.1109/ISWC.2009.23}, author = {Van Laerhoven, Kristof and Berlin, Eugen} } @inproceedings {ess_icmla2009, title = {Enabling Efficient Time Series Analysis for Wearable Activity Data}, booktitle = {Proceedings of the 8th International Conference on Machine Learning and Applications (ICMLA 2009)}, year = {2009}, pages = {392-397}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Miami Beach, FL, USA}, doi = {10.1109/ICMLA.2009.112}, author = {Van Laerhoven, Kristof and Berlin, Eugen and Schiele, Bernt} } @inproceedings {mis_ISWC2008a, title = {Exploring Semi-Supervised and Active Learning for Activity Recognition}, booktitle = {Proceedings of the 12th International Symposium on Wearable Computers (ISWC 2008)}, year = {2008}, month = {September}, pages = {81-90}, publisher = {IEEE Press}, organization = {IEEE Press}, doi = {10.1109/ISWC.2008.4911590}, address = {Pittsburgh, USA}, author = {Stikic, Maja and Van Laerhoven, Kristof and Schiele, Bernt} } @inproceedings {mis_phealth2008, title = {{ADL Recognition Based on the Combination of RFID and Accelerometer Sensing}}, booktitle = {Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health 2008)}, year = {2008}, month = {January}, pages = {258{\textendash}263}, publisher = {IEEE Xplore}, organization = {IEEE Xplore}, doi={10.1109/PCTHEALTH.2008.4571084}, address = {Tampere, Finland}, author = {Stikic, Maja and Huynh, T{\^a}m and Van Laerhoven, Kristof and Schiele, Bernt} } @inproceedings {mis_ISWC2008b, title = {Sustained Logging and Discrimination of Sleep Postures with Low-Level, Wrist-Worn Sensors}, booktitle = {Proceedings of the 12th International Symposium on Wearable Computers (ISWC 2008)}, year = {2008}, pages = {69-77}, publisher = {IEEE Press}, organization = {IEEE Press}, doi = {10.1109/ISWC.2008.4911588}, abstract = {We present a study which evaluates the use of simple low-power sensors for a long-term, coarse-grained detection of sleep postures. In contrast to the information-rich but complex recording methods used in sleep studies, we follow a paradigm closer to that of actigraphy by using a wrist-worn device that continuously logs and processes data from the user. Experiments show that it is feasible to detect nightly sleep periods with a combination of light and simple motion and posture sensors, and to detect within these segments what basic sleeping postures the user assumes. These find- ings can be of value in several domains, such as monitoring of sleep apnea disorders, and support the feasibility of a continuous home-monitoring of sleeping trends where users wear the sensor device uninterruptedly for weeks to months in a row. }, author = {Van Laerhoven, Kristof and Borazio, Marko and Kilian, David and Schiele, Bernt} } @inproceedings {mis_ISWC2008c, title = {Using Rhythm Awareness in Long-Term Activity Recognition}, booktitle = {Proceedings of the 12th International Symposium on Wearable Computers (ISWC 2008)}, year = {2008}, pages = {63-68}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Pittsburgh, PA, USA}, abstract = {This paper reports on research where users{\textquoteright} activities are logged for extended periods by wrist-worn sensors. These devices operated for up to 27 consecutive days, day and night, while logging features from motion, light, and temperature. This data, labeled via 24-hour self-recall annotation, is explored for occurrences of daily activities. An evaluation shows that using a model of the users{\textquoteright} rhythms can improve recognition of daily activities significantly within the logged data, compared to models that exclusively use the sensor data for activity recognition.}, isbn = {978-1-4244-2637-9}, doi = {10.1109/ISWC.2008.4911586}, author = {Van Laerhoven, Kristof and Kilian, David and Schiele, Bernt} } @inproceedings {SMC2008Berchtold, title = {Gath-Geva Specification and Genetic Generalization of Takagi-Sugeno-Kang Fuzzy Models}, booktitle = {Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2008)}, year = {2008}, publisher = {IEEE Press}, doi={10.1109/ICSMC.2008.4811342}, organization = {IEEE Press}, author = {Berchtold, Martin and Riedel, Till and Decker, Christian and Van Laerhoven, Kristof} } @inproceedings {WSNHC2007stikic, title = {Recording Housekeeping Activities with Situated Tags and Wrist-Worn Sensors: Experiment Setup and Issues Encountered}, booktitle = {Proceedings of the 1st International Workshop on Wireless Sensor Networks for Health Care (WSNHC 2007)}, year = {2007}, month = {June}, address = {Braunschweig, Germany}, abstract = {RFID tag readers and accelerometers are two sensing technologies that have recently dropped in both size and cost. Assuming that key household items can easily be tagged, one could legitimately imagine a wrist-worn sensor which incorporates both, to infer Activities of Daily Living (ADL). This paper presents initial challenges in research towards this scenario by describing critical choices made in a series of data recording experiments, in which we intend to capture realistic sensor data in a lab setting, using a set of housekeeping activities as targets.}, author = {Stikic, Maja and Van Laerhoven, Kristof} } @inproceedings {usab2007Silva, title = {BadIdeas for Usability and Design of Medicine and Healthcare Sensors}, booktitle = {Proceedings of the 3rd Human-computer interaction and usability engineering of the Austrian computer society conference on HCI and usability for medicine and health care (USAB{\textquoteright}07)}, volume = {4799}, year = {2007}, month = {11/2007}, pages = {105{\textendash}112}, publisher = {Springer}, doi = {10.1007/978-3-540-76805-0_8}, organization = {Springer}, address = {Graz, Austria}, abstract = {This paper describes the use of a technique to improve design and to develop new uses and improve usability of user interfaces. As a case study, we focus on the design and usability of a research prototype of an actigraph - electronic activity and sleep study device - the Porcupine. The proposed BadIdeas technique was introduced to a team of students who work with this sensor and the existing design was analysed using this technique. The study found that the BadIdeas technique has promising characteristics that might make it an ideal tool in the prototyping and design of usability-critical appliances. }, isbn = {3-540-76804-1, 978-3-540-76804-3}, author = {Silva, Paula Alexandra and Van Laerhoven, Kristof} } @inproceedings {mis_ami2007, title = {Toward Recognition of Short and Non-repetitive Activities from Wearable Sensors}, booktitle = {European Conference on Ambient Intelligence (AmI 2007)}, series = {Lecture Notes in Computer Science}, volume = {4794}, year = {2007}, month = {11/2007}, pages = {142-158}, publisher = {Springer}, organization = {Springer}, address = {Darmstadt, Germany}, abstract = {Activity recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware computing. Most approaches for activity recognition focus on repetitive or long time patterns within the data. There is however high interest in recognizing very short activities as well, such as pushing and pulling an oil stick or opening an oil container as sub-tasks of checking the oil level in a car. This paper presents a method for the latter type of activity recognition using start and end postures (short fixed positions of the wrist) in order to identify segments of interest in a continuous data stream. Experiments show high discriminative power for using postures to recognize short activities in continuous recordings. Additionally, classifications using postures and HMMs for recognition are combined. }, isbn = {978-3-540-76651-3}, doi = {10.1007/978-3-540-76652-0_9}, author = {Zinnen, Andreas and Van Laerhoven, Kristof and Schiele, Bernt} } @inproceedings {mis_icdcsw2007, title = {Memorizing What You Did Last Week: Towards Detailed Actigraphy With A Wearable Sensor}, booktitle = {27th International Conference on Distributed Computing Systems Workshops (ICDCSW 2007)}, year = {2007}, pages = {47}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Toronto, Ontario, Canada}, abstract = {With sensors becoming smaller and more power efficient, wearable sensors that anyone could wear are becoming a feasible concept. We demonstrate a small lightweight module, called Porcupine, which aims at continuously monitoring human activities as long as possible, and as fine-grained as possible. We present initial analysis of a set of abstraction algorithms that combine and process raw accelerometer data and tilt switch states, to get descriptors of the user{\textquoteright}s motion- based activities. The algorithms are running locally, and the information they produce is stored in on-board memory for later analysis.}, isbn = {0-7695-2838-4}, doi = {10.1109/ICDCSW.2007.43}, author = {Van Laerhoven, Kristof and Aronsen, Andre} } @inproceedings {lancs_icdcsw2006, title = {Fair Dice: A Tilt and Motion-Aware Cube with a Conscience.}, booktitle = {26th International Conference on Distributed Computing Systems Workshops (ICDCSW 2006)}, year = {2006}, month = {07/2006}, pages = {66}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, abstract = {As an example of sensory augmentation of a tiny object, a small cube-sized die is presented that perceives rolls and records what face it lands on. It is thus able to detect bias for unfair behaviour due to its physical imperfections. On a deeper level, this case study demonstrates the integration of energy-efficient sensor fusion, combination of classifiers, and a wireless interface to adaptive classification heuristics.}, isbn = {0-7695-2541-5}, doi = {10.1109/ICDCSW.2006.50 }, author = {Van Laerhoven, Kristof and Hans-Werner Gellersen} } @inproceedings {lancs_bsn2006, title = {Long-Term Activity Monitoring with a Wearable Sensor Node}, booktitle = {International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006)}, year = {2006}, month = {04/2006}, pages = {171{\textendash}174}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Cambridge, Massachusetts, USA}, abstract = {This paper introduces an encapsulated sensor node that is devised to monitor and record motion patterns over long, quotidian periods of time with potential application in psychological studies. Its design fuses different sensing modalities to allow efficient capturing of tilt and acceleration stimuli, as well as embedded algorithms that abstract from the raw sensory data to indicative features. By combining tilt switches and accelerometers with customized processing techniques, it is argued that a power-efficient yet information-rich approach is reached for the observation and logging of human motion-based activity.}, isbn = {0-7695-2547-4}, doi = {10.1109/BSN.2006.39}, author = {Van Laerhoven, Kristof and Hans-Werner Gellersen and Malliaris, Yanni G.} } @phdthesis {lancs_es2006, title = {Embedded Perception - Concept Recognition by Learning and Combining Sensory Data}, volume = {Ph.D.}, year = {2006}, month = {03/2006}, school = {Lancaster University}, address = {Lancaster}, author = {Van Laerhoven, Kristof} } @inproceedings {lancs_bsn2005, title = {Real-Time Analysis of Correlations Between On-Body Sensor Nodes}, booktitle = {Proceedings of the 2nd International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2005)}, year = {2005}, pages = {27-32}, publisher = {IEE Press}, organization = {IEE Press}, abstract = {The topology of a body sensor network has, until recently, often been overlooked; either because the layout of the network is deemed to be sufficiently static ("we always know well enough where sensors are"), we always know exactly where the nodes are or because the location of the sensor is not inherently required ("as long as the node stays where it is, we do not need its location, just its data"). We argue in this paper that, especially as the sensor nodes become more numerous and densely interconnected, an analysis on the correlations between the data streams can be valuable for a variety of purposes. Two systems illustrate how a mapping of the network{\textquoteright}s sensor data to a topology of the sensor nodes{\textquoteright} correlations can be applied to reveal more about the physical structure of body sensor networks.}, isbn = {0-86341-505-9}, doi = {10.1.1.61.9134}, author = {Van Laerhoven, Kristof and Berchtold, Martin} } @inproceedings {lancs_ie2005, title = {The Pendle: A Personal Mediator for Mixed Initiative Environments}, booktitle = {IEE International Workshop on Intelligent Environments}, year = {2005}, publisher = {IEE Press}, organization = {IEE Press}, address = {Essex, UK}, abstract = {In this paper we propose a novel interaction model for augmented environments based on the concept of mixed initiative interaction, and describe the design of the Pendle, a gesture- based wearable device. By splitting control between user and environment, the interaction model combines the advantages of explicit, direct manipulation with the power of sensor-based proactive environments while avoiding the lack of user control and personalization usually associated with the later. The Pendle is a personalizable wearable device with the capability to recognize hand gestures. It acts as mediator between user and environment, and provides a simple, natural interface that lends itself to casual interaction. Experiences with two concrete examples, the MusicPendle and NewsPendle, demonstrate the advantages of the personalized user experience and the flexibility of the device architecture.}, doi = {10.1.1.60.4555}, author = {Nicolas Villar and Kortuem, Gerd and Van Laerhoven, Kristof and Schmidt, Albrecht} } @inproceedings {lancs_ubihealth2004, title = {Medical Healthcare Monitoring with Wearable and Implantable Sensors}, booktitle = {International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications (UbiHealth)}, year = {2004}, month = {September}, abstract = {The last decade has witnessed a surge of interest in new sensing and monitoring devices for healthcare, with implantable in vivo monitoring and intervention devices being key developments in this area. Permanent implants combined with wearable monitoring devices could provide continuous assessment of critical physiological parameters for identifying precursors of major adverse events. Open research issues in this area are predominantly related to novel sensor interface design, practical and reliable distributed com- puting environments for multi-sensory data fusion, but the concept itself is deemed to have further impact in many other areas. This position paper describes a scenario from the UbiMon [1] project, which is aimed at investigating healthcare delivery by combining wearable and implantable sensors.}, author = {Van Laerhoven, Kristof and Benny PL Lo and Jason WP Ng and Surapa Thiemjarus and Rachel King and Simon Kwan and Hans-Werner Gellersen and Morris Sloman and Oliver Wells and Phil Needham and Nick Peters and Ara Darzi and Chris Toumazou and Guang-Zhong Yang} } @inproceedings {lancs_iswc2004, title = {Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition}, booktitle = {8th International Symposium on Wearable Computers (ISWC 2004)}, year = {2004}, pages = {142-149}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, abstract = {This paper seeks to explore an alternative and more embedded-oriented approach to the recognition of a person{\textquoteright}s motion and pose, using sensor types that can easily be distributed in clothing. A large proportion of this type of research so far has been carried out with carefully positioned accelerometers, resulting in fairly good recognition rates. An alternative approach targets a more pervasive sensing vision where the clothing is saturated with small, embedded sensors. By increasing the quantity of sensors, while decreasing their individual information quality, a preliminary comparative study between the two approaches looks at the pros, cons, and differences in algorithm requirements.}, doi = {10.1109/ISWC.2004.40}, author = {Van Laerhoven, Kristof and Hans-Werner Gellersen} } @inproceedings {lancs_ucs2004, title = {The Pervasive Sensor - Invited Talk}, booktitle = {Ubiquitous Computing Systems, Second International Symposium, UCS 2004}, year = {2004}, pages = {1-9}, publisher = {Springer Verlag}, organization = {Springer Verlag}, abstract = {Forget processing power, memory, or the size of computers for a moment: Sensors, and the data they provide, are as important as any of these factors in realising ubiquitous and pervasive computing. Sensors have already become influential components in newer applications, but their data needs to be used more intelligently if we want to unlock their true potential. This requires improved ways to design and integrate sensors in computer systems, and interpret their signals.}, isbn = {3-540-27893-1}, doi = {10.1007/11526858_1}, author = {Van Laerhoven, Kristof} } @inproceedings {lancs_per2004, title = {Augmenting Collections of Everyday Objects: A Case Study of Clothes Hangers As an Information Display}, booktitle = {Pervasive Computing, Second International Conference}, series = {Lecture Notes in Computer Science}, volume = {3001}, year = {2004}, note = {oral, 12\% acceptance rate}, pages = {340-344}, publisher = {Springer}, organization = {Springer}, abstract = {Though the common conception of human-computer interfaces is one of screens and keyboards, the emergence of ubiquitous computing envisions interfaces that will spread from the desktop into our environments. This gives rise to the development of novel interaction devices and the augmentation of common everyday objects to serve as interfaces between the physical and the virtual. Previous work has provided exemplars of such everyday objects augmented with interactive behaviour. We propose that richer opportunities arise when collections of everyday objects are considered as substrate for interfaces. In an initial case study we have taken clothes hangers as an example and augmented them to collectively function as an information display. }, isbn = {3-540-21835-1}, doi = {10.1007/978-3-540-24646-6_24}, author = {Tara Matthews and Hans-Werner Gellersen and Van Laerhoven, Kristof and Dey, Anind K.} } @inproceedings {lancs_eusai2004, title = {A Physical Notice Board with Digital Logic and Display}, booktitle = {Adjunct Proceedings of the European Symposium on Ambient Intelligence 2004}, year = {2004}, pages = {207-217}, publisher = {ACM Press}, organization = {ACM Press}, abstract = {A physical notice board is augmented with digital capabilities to provide additional functionality. The notice board retains its form factor, and when used with interactive pins with controllable lights it can be used to signal to the user information about the state of documents posted on the board. Both board and augmented pins are easy to deploy and cheap to produce.}, doi = {10.1145/1031419.1031428}, author = {Nicolas Villar and Van Laerhoven, Kristof and Hans-Werner Gellersen} } @inproceedings {lancs_eurowearable2003c, title = {Towards an Intertial Sensor Network}, booktitle = {IEE EuroWearable}, year = {2003}, month = {September}, address = {Birmingham, UK}, abstract = {Wearable inertial sensors have become an inexpensive option to measure the movements and positions of a person. Other techniques that use environmental sensors such as ultrasound trackers or vision-based methods need full line of sight or a local setup, and it is complicated to access this data from a wearable computer{\textquoteright}s perspective. However, a body-centric approach where sensor data is acquired and processed locally, has a need for appropriate algorithms that have to operate under restricted resources. The objective of this paper is to give an overview of algorithms that abstract inertial data from body-worn sensors, illustrated using data from state-of-the-art wearable multi-accelerometer prototypes. }, doi = {10.1049/ic:20030158}, author = {Kern, Nicky and Van Laerhoven, Kristof and Hans-Werner Gellersen and Schiele, Bernt} } @inproceedings {lancs_eurowearable2003b, title = {Exploring Cube Affordance: Towards A Classification Of Non-Verbal Dynamics Of Physical Interfaces For Wearable Computing}, booktitle = {IEE EuroWearable 2003}, year = {2003}, month = {September}, pages = {113-118}, abstract = {Current input technologies for wearable computers are difficult to use and learn and can be unreliable. Physical interfaces offer an alternative to traditional input methods. In this paper we propose that developing a well-designed physical interface requires an exploration of the psychological idea of affordance. We present our findings from a design study in which we explore the natural affordance of a cube and suggest possible requirements for the design of graspable cube-shaped physical interfaces as alternative rich-action input device. We expect that such a framework will enhance the precision and usability of devices for wearable and mobile computing.}, doi = {10.1049/ic:20030156}, author = {Jennifer Sheridan and Ben Short and Kortuem, Gerd and Van Laerhoven, Kristof and Nicolas Villar} } @inproceedings {lancs_eurowearable2003a, title = {A Layered Approach to Wearable Textile Networks}, booktitle = {IEE EuroWearable 2003}, year = {2003}, month = {September}, pages = {61-67}, abstract = {The integration of digital components into clothing is becoming an increasingly important segment in wearable computing research. The first indications for this trend are the incorporation of existing mobile technologies, such as personal digital assistants (PDAs) or mobile phones, into jackets via flexible textile circuits. In the long term, other components could also be envisioned that are embedded in apparel, using a flexible bus-type network that links all the devices together. This paper introduces a functioning prototype of such a flexible network that not only allows communication between wearable components, but is also able to supply power to them. We propose an arrangement of layered textiles as opposed to the more traditional routed circuitry layout, which results in a novel approach towards the concept of a flexible clothing network.}, doi = {10.1049/ic:20030148}, author = {Van Laerhoven, Kristof and Nicolas Villar and Hans-Werner Gellersen} } @inproceedings {lancs_icmi2003, title = {Using an autonomous cube for basic navigation and input}, booktitle = {Proceedings of the 5th International Conference on Multimodal Interfaces, ICMI 2003}, year = {2003}, pages = {203-210}, publisher = {ACM}, organization = {ACM}, abstract = {This paper presents a low-cost and practical approach to achieve basic input using a tactile cube-shaped object, augmented with a set of sensors, processor, batteries and wireless communication. The algorithm we propose combines a finite state machine model incorporating prior knowledge about the symmetrical structure of the cube, with maximum likelihood estimation using multivariate Gaussians. The claim that the presented solution is cheap, fast and requires few resources, is demonstrated by implementation in a small-sized, microcontroller-driven hardware configuration with inexpensive sensors. We conclude with a few prototyped applications that aim at characterizing how the familiar and elementary shape of the cube allows it to be used as an interaction device.}, isbn = {1-58113-621-8}, doi = {10.1145/958432.958472}, author = {Van Laerhoven, Kristof and Nicolas Villar and Schmidt, Albrecht and Kortuem, Gerd and Hans-Werner Gellersen} } @article {lancs_ieeecomm2003, title = {Pin{&}Play: The Surface as Network Medium}, journal = {IEEE Communications}, volume = {41}, number = {4}, year = {2003}, pages = {90-96}, publisher = {IEEE Press}, abstract = {Integrating appliances in the home through a wired network often proves to be impractical: routing cables is usually difficult, changing the network structure afterward even more so, and portable devices can only be connected at fixed connection points. Wireless networks are not the answer either: batteries have to be regularly replaced or changed, and what they add to the device{\textquoteright}s size and weight might be disproportionate for smaller appliances. In Pin&Play, we explore a design space in between typical wired and wireless networks, investigating the use of surfaces to network objects that are attached to it. This article gives an overview of the network model, and describes functioning prototypes that were built as a proof of concept.}, doi = {10.1109/MCOM.2003.1193980}, author = {Van Laerhoven, Kristof and Nicolas Villar and Schmidt, Albrecht and Hans-Werner Gellersen and Holmquist, Lars Erik} } @inproceedings {lancs_iswc2002, title = {Multi-Sensor Context Aware Clothing}, booktitle = {6th International Symposium on Wearable Computers (ISWC 2002)}, year = {2002}, note = {oral, 19\% acceptance rate}, pages = {49-56}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, abstract = {Inspired by perception in biological systems, distribution of a massive amount of simple sensing devices is gaining more support in detection applications. A focus on fusion of sensor signals instead of strong analysis algorithms, and a scheme to distribute sensors, results in new issues. Especially in wearable computing, where sensor data continuously changes, and clothing provides an ideal supporting structure for simple sensors, this approach may prove to be favourable. Experiments with a body-distributed sensor system investigate the influence of two factors that affect classification of what has been sensed: an increase in sensors enhances recognition, while adding new classes or contexts depreciates the results. Finally, a wearable computing related scenario is discussed that exploits the presence of many sensors. }, isbn = {0-7695-1816-8}, doi = {10.1109/ISWC.2002.1167218}, author = {Van Laerhoven, Kristof and Schmidt, Albrecht and Hans-Werner Gellersen} } @inproceedings {lancs_ubicomp2002, title = {Pin{&}Play: Networking Objects through Pins}, booktitle = {UbiComp 2002: Ubiquitous Computing, 4th International Conference}, series = {Lecture Notes in Computer Science}, volume = {2498}, year = {2002}, note = {oral, 18\% acceptance rate}, pages = {219-228}, publisher = {Springer}, organization = {Springer}, abstract = {We introduce a new concept of networking objects in everyday environments. The basic idea is to build on the familiar use of surfaces such as walls and boards for attachment of mundane objects such as light controls, pictures, and notes. Hence our networking concept entails augmentation of such surfaces with conductive material to enable them as communication medium. It further incorporates the use of simple pushpin-connectors through which objects can be attached to network-enabled surfaces. Thereby users are provided with a highly familiar mechanism for adding objects ad hoc to the bus network, hence its name Pin&Play. This paper describes the architecture and principles of Pin&Play, as well as the design and implementation of a smart notice-board as proof of concept. }, isbn = {3-540-44267-7}, doi = {10.1007/3-540-45809-3_17}, author = {Van Laerhoven, Kristof and Schmidt, Albrecht and Hans-Werner Gellersen} } @inproceedings {lancs_ubicomp2002b, title = {Context Acquisition Based on Load Sensing}, booktitle = {UbiComp 2002: Ubiquitous Computing, 4th International Conference}, series = {Lecture Notes in Computer Science}, volume = {2498}, year = {2002}, note = {oral, 18\% acceptance rate}, pages = {333-350}, publisher = {Springer}, organization = {Springer}, abstract = {Load sensing is a mature and robust technology widely applied in process control. In this paper we consider the use of load sensing in everyday environments as an approach to acquisition of contextual information in ubiquitous computing applications. Since weight is an intrinsic property of all physical objects, load sensing is an intriguing concept on the physical-virtual boundary, enabling the inclusive use of arbitrary objects in ubiquitous applications. In this paper we aim to demonstrate that load sensing is a versatile source of contextual information. Using a series of illustrative experiments we show that using load sensing techniques we can obtain not just weight information, but object position and interaction events on a given surface. We describe the incorporation of load-sensing in the furniture and the floor of a living laboratory environment, and report on a number of applications that use context information derived from load sensing. }, isbn = {3-540-44267-7}, doi = {10.1007/3-540-45809-3_26}, author = {Schmidt, Albrecht and Strohbach, Martin and Van Laerhoven, Kristof and Adrian Friday and Hans-Werner Gellersen} } @inproceedings {lancs_uia2002, title = {Ubiquitous Interaction - Using Surfaces in Everyday Environments as Pointing Devices}, booktitle = {Universal Access: Theoretical Perspectives, Practice, and Experience, 7th ERCIM International Workshop on User Interfaces for All}, series = {Lecture Notes in Computer Science}, volume = {2615}, year = {2002}, pages = {263-279}, publisher = {Springer}, organization = {Springer}, abstract = { To augment everyday environments as interface to computing may lead to more accessible and inclusive user interfaces, exploiting affordances existing in the physical world for interaction with digital functionality. A major challenge for such interfaces is to preserve accustomed uses while providing unobtrusive access to new services. In this paper we discuss augmentation of common surfaces such as tables as generic pointing device. The basic concept is to sense the load, the load changes and the patterns of change observed on a surface using embedded load sensors. We describe the interaction model used to derive pointing actions from basic sensor observations, and detail the technical augmentation of two ordinary tables that we used for our experiments. The technology effectively emulates a serial mouse, and our implementation and use experience prove that it is unobtrusive, robust, and both intuitively and reliably usable.}, isbn = {3-540-00855-1}, doi = {10.1007/3-540-36572-9_21}, author = {Schmidt, Albrecht and Strohbach, Martin and Van Laerhoven, Kristof and Hans-Werner Gellersen} } @inproceedings {lancs_wna2002, title = {Pin{&}Play: Bringing Power and Networking to Wall-Mounted Appliances}, booktitle = {Proceedings of the 5th IEE International Workshop on Networked Appliances, Liverpool, UK}, year = {2002}, pages = {131-137}, publisher = {IEEE Press}, organization = {IEEE Press}, doi = {10.1109/IWNA.2002.1241349}, author = {Van Laerhoven, Kristof and Nicolas Villar and Maria Hakansson and Hans-Werner Gellersen} } @inproceedings {LANCS_AIMS2002, title = {Context Awareness in Systems with Limited Resources}, booktitle = {Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS), ECAI 2002}, year = {2002}, pages = {21-29}, abstract = {Mobile embedded systems often have strong limitations regarding available resources. In this paper we propose a statistical approach which could scale down to microcontrollers with scarce resources, to model simple contexts based on raw sensor data. As a case study, two experiments are provided where statistical modeling techniques were applied to learn and recognize different contexts, based on accelerometer data. We furthermore point out applications that utilize contextual information for power savings in mobile embedded systems.}, author = {Ozan Cakmakci and Joelle Coutaz and Van Laerhoven, Kristof and Hans-Werner Gellersen} } @article {sl_ieeepc2001, title = {How to Build Smart Appliances?}, journal = {IEEE Personal Communications}, journalurl={\url{http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=20430}}, volume = {8}, number = {4}, year = {2001}, month = {08/2001}, pages = {66 {\textendash} 71}, abstract = {In this article smart appliances are characterized as devices that are attentive to their environment. We introduce a terminology for situation, sensor data, context, and context-aware applications because it is important to gain a thorough understanding of these concepts to successfully build such artifacts. In the article the relation between a real-world situation and the data read by sensors is discussed; furthermore, an analysis of available sensing technology is given. Then we introduce an architecture that supports the transformation from sensor data to cues then to contexts as a foundation to make context-aware applications. The article suggests a method to build context-aware devices; the method starts from situation analysis, offers a structured way for selection of sensors, and finally suggests steps to determine recognition and abstraction methods. In the final part of the article the question of how this influences the applications is raised and the areas of user interfaces, communication, and proactive application scheduling are identified. We conclude with the description of a case study where a mobile phone was made aware of its environment using different sensors. The profile settings of the phone (ringing mode etc.) are automatically selected according to the real world situation the phone is used in.}, issn = {1070-9916}, doi = {10.1109/98.944006}, author = {Schmidt, Albrecht and Van Laerhoven, Kristof} } @inproceedings {sl_icann2001, title = {Combining the Self-Organizing Map and K-Means Clustering for On-Line Classification of Sensor Data}, booktitle = {Artificial Neural Networks - ICANN 2001, International Conference}, bookurl={\url{http://dl.acm.org/citation.cfm?id=646258}}, series = {Lecture Notes in Computer Science}, volume = {2130}, year = {2001}, pages = {464-469}, publisher = {Springer}, organization = {Springer}, abstract = {Many devices, like mobile phones, use contextual profiles like "in the car" or "in a meeting" to quickly switch between behaviors. Achieving automatic context detection, usually by analysis of small hardware sensors, is a fundamental problem in human-computer interaction. However, mapping the sensor data to a context is a difficult problem involving near real-time classification and training of patterns out of noisy sensor signals. This paper proposes an adaptive approach that uses a Kohonen Self-Organizing Map, augmented with on-line k-means clustering for classification of the incoming sensor data. Overwriting of prototypes on the map, especially during the untangling phase of the Self-Organizing Map, is avoided by a refined k-means clustering of labeled input vectors.}, abstracturl={\url{http://dl.acm.org/citation.cfm?id=683987}}, isbn = {3-540-42486-5}, doi = {10.1007/3-540-44668-0_65}, author = {Van Laerhoven, Kristof} } @inproceedings {sl_iswc2001, title = {Real-time Analysis of Data from Many Sensors with Neural Networks}, booktitle = {5th International Symposium on Wearable Computers (ISWC 2001)}, year = {2001}, pages = {115-122}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, abstract = {Much research has been conducted that uses sensor- based modules with dedicated software to automatically distinguish the user{\textquoteright}s situation or context. The best results were obtained when powerful sensors (such as cameras or GPS systems) and/or sensor-specific algorithms (like sound analysis) were applied. A somewhat new approach is to replace the one smart sensor by many simple sensors. We argue that neural networks are ideal algorithms to analyze the data coming from these sensors and describe how we came to one specific algorithm that gives good results, by giving an overview of several requirements. Finally, wearable implementations are given to show the feasibility and benefits of this approach and its implications.}, bookurl={\url{http://dl.acm.org/citation.cfm?id=580581}}, isbn = {0-7695-1318-2}, doi = {10.1109/ISWC.2001.962112}, author = {Van Laerhoven, Kristof and Kofi A Aidoo and Steven Lowette} } @article {sl_puc2001, title = {Teaching Context to Applications}, journal = {Personal and Ubiquitous Computing}, journalurl={\url{http://www.springer.com/computer/hci/journal/779}}, volume = {5}, number = {1}, year = {2001}, pages = {46-49}, publisher = {Springer Verlag}, address = {London, UK}, abstract = {Although mobile devices keep getting smaller and more powerful, their interface with the user is still based on that of the regular desktop computer. This implies that interaction is usually tedious, while interrupting the user is not really desired in ubiquitous computing. We propose adding an array of hardware sensors to the system that, together with machine learning techniques, make the device aware of its context while it is being used. The goal is to make it learn the context-descriptions from its user on the spot, while minimising user-interaction and maximising reliability.}, issn = {1617-4909}, doi = {10.1007/s007790170029}, author = {Van Laerhoven, Kristof and Kofi A Aidoo} } @inproceedings {sl_iswc2000, title = {What Shall We Teach Our Pants?}, booktitle = {Proceedings of the 4th IEEE International Symposium on Wearable Computers (ISWC)}, bookurl={\url{http://dl.acm.org/citation.cfm?id=851037}}, year = {2000}, pages = {77}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Washington, DC, USA}, abstract = {If a wearable device can register what the wearer is currently doing, it can anticipate and adjust its behavior to avoid redundant interaction with the user. However, the relevance and properties of the activities that should be recognized depend on both the application and the user. This requires an adaptive recognition of the activities where the user, instead of the designer, can teach the device what he/she is doing. As a case study we connected a pair of pants with accelerometers to a laptop to interpret the raw sensor data. Using a combination of machine learning techniques such as Kohonen maps and probabilistic models, we build a system that is able to learn activities while requiring minimal user attention. This approach to context awareness is more universal since it requires no a priori knowledge about the contexts or the user. }, isbn = {0-7695-0795-6}, doi = {10.1109/ISWC.2000.888468}, author = {Van Laerhoven, Kristof and Ozan Cakmakci} } @mastersthesis{mscthesis_laerhoven, title ={On-line adaptive context awareness starting from low-level sensors}, author={Van Laerhoven, Kristof}, abstract={Along with the sales-figures and the popularity of wearable and portable devices, the importance of their usability and functionality is increasing as well. Most of these devices need to change their behavior according to the context they are currently in. Inadequate knowledge about the context results in a lack of user-friendliness. Mobile phones for example don?t know when and how to disturb their users when a call arrives. The solution would be to add various sensors and thus give the host device more knowledge about its context. However, the way humans describe contexts is not by giving the complete inventory of their sensations. Often, either unusual elements ("It is cold.") or more abstract properties ("I am home.", "I am in a dark room." ) are noticed and expressed. If the device needs to function in a transparent way for its user or if the user needs to train the device, it needs to recognize contexts in a similar way. This thesis will focus on the transformation of a multitude of sensory information to a short context description, supplied by the user, in an adaptive and on-line way. The approach is to use an adaptive hybrid system consisting of a connectionist layer, followed by a symbolic layer. Sensor outputs will be periodically sent to a self-organizing artificial neural network architecture, which is responsible for the initial processing and clustering. A symbolic layer, implemented as a Markov chain, provides a predictive component that enables interaction with the user, ensures an enhanced recognition.}, school = {University of Brussels (VUB)}, month = {7}, year = {1999} } @inproceedings {sl_huc1999, title = {Advanced Interaction in Context}, booktitle = {The First International Symposium on Handheld and Ubiquitous Computing (HUC'99)}, publisher = {Springer Verlag}, month = {9}, year = {1999}, pages = {89-101}, address = {Karlsruhe, Germany}, abstract = {Mobile information appliances are increasingly used in numerous different situations and locations, setting new requirements to their interaction methods. When the user{\textquoteright}s situation, place or activity changes, the functionality of the device should adapt to these changes. In this work we propose a layered real-time architecture for this kind of context-aware adaptation based on redundant collections of low-level sensors. Two kinds of sensors are distinguished: physical and logical sensors, which give cues from environment parameters and host information. A prototype board that consists of eight sensors was built for experimentation. The contexts are derived from cues using real-time recognition software, which was constructed after experiments with Kohonen{\textquoteright}s Self-Organizing Maps and its variants. A personal digital assistant (PDA) and a mobile phone were used with the prototype to demonstrate situational awareness. On the PDA font size and backlight were changed depending on the demonstrated contexts while in mobile phone the active user profile was changed. The experiments have shown that it is feasible to recognize contexts using sensors and that context information can be used to create new interaction metaphors.}, isbn = {3-540-66550-1}, doi = {10.1007/3-540-48157-5_10}, bookurl={\url{http://dl.acm.org/citation.cfm?id=647985}}, author = {Schmidt, Albrecht and Kofi A Aidoo and Antti Takaluoma and Urpo Tuomela and Van Laerhoven, Kristof and Walter Van de Velde} }