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Datensätze

The following data sets have been recorded during our research and are being made available for download*:

Sensors 2019

Deep PPG: Large-scale Heart Rate Estimation with Convolutional Neural Networks Authors: Attila Reiss, Ina Indlekofer, Philip Schmidt, and Kristof Van Laerhoven. MDPI Sensors 2019.
A large dataset with a wide range of physical activities, performed under close to real-life conditions.

ICMI 2018

Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, and Kristof Van Laerhoven. ICMI’18, Boulder, Colorado, ACM, 2018.
Physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study.

UbiComp 2015

Wearables in the Wetlab: a Laboratory System for Capturing and Guiding Experiments. Philipp M. Scholl, Matthias Wille and Kristof Van Laerhoven, UbiComp'15, Osaka, Japan, ACM, 2015.
Original video recordings, activities encoded as subtitles and acceleration data of the wrist for 22 participants.

ICHI 2014

Towards a Benchmark for Wearable Sleep Analysis with Inertial Wrist-worn Sensing Units, Marko Borazio, Eugen Berlin, Nagihan Kücükyildiz, Philipp M. Scholl and Kristof Van Laerhoven. ICHI 2014. IEEE Press.
Recorded PSG and inertial data from 42 sleep lab patients and scripts to visualize all data.

AH 2013

Time Use Surveys: Improving Activity Recognition without Sensor Data, Marko Borazio and Kristof Van Laerhoven, ACM AH 2013.
Experiment scripts for using time surveys and activity episodes from 5160 households / 13798 individuals.

UbiComp 2012

Detecting Leisure Activities with Dense Motif Discovery, Eugen Berlin and Kristof Van Laerhoven, Ubicomp 2012. ACM Press.
Raw sensor data from 6 participants wearing our wrist-worn prototype for a full week.

INSS 2012

Trainspotting: Combining Fast Features to Enable Detection on Resource-constrained Sensing Devices, Eugen Berlin and Kristof Van Laerhoven, INSS 2012, IEEE Press.
Vibration footprint data from rail-based wireless sensing nodes, for 250 trains of different types.



[More to be added at a later date]

*Please note that although all our data sets are being published here without any restrictions (they are free to download, free to use and to publish about without special requirements from our side) by default, some of them might be subject to stricter control because of the nature of the data or project they were funded by. See the individual data set's descriptions for more information.