Improving Activity Recognition without Sensor Data
Wearable sensing systems, through their proximity with their user, can be used to automatically infer the wearer’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.
In this project we use a strategy first suggested by Partridge and Golle  that utilizes time use diaries in an activity recognition method. We offer an evaluation of the German Time Use database, showing that certain important features could be useful for activity recognition. By cross-validating across the 5160 households in this new data with activity episodes of 13798 individuals, especially distinctive features turn out to be time and participant’s location. The scripts used in this study can be obtained to be used on the German Time Use Study from 2001/2002. Further details can be found in the paper.
Fig. 1: Depicting per time-of-day the normalized occurrences of common activities over all participants from the German Time Use Survey of 2001/2002.
 K. Partridge and P. Golle, "On Using Existing Time-Use Study Data for Ubiquitous Computing Applications". UbiComp'08
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Citation / first paper
Marko Borazio and Kristof Van Laerhoven, "Improving Activity Recognition without Sensor Data: A Comparison Study of Time Use Surveys". 4th International Augmented Human Conference (AH2013), ACM Press, 2013.
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