Unsupervised monitoring of sitting behavior.
In recent years, the monitoring of sitting postures was discovered to be a promising measure of healthy sitting behavior, comfort, physical wellness and emotions. Most state-of-the-art systems for monitoring sitting behavior are based on supervised methods that are limited to a fixed set of classes. We present a method that does not rely on training but distinguishes between different postures autonomously. We designed and implemented a system to monitor sitting behavior in an unsupervised manner. Based on the pressure distribution acquired from a pressure mat we generate prototypes of sitting postures. The prototypes are stored in a database and serve as reference for comparing and classifying incoming pressure data. The system relies on only a few, interpretable system parameters and performs in real-time. We conducted an experiment with a collective of 8 subjects and recorded the data of 16 different postures for each subject. Our proposed method generates on average 15.57 prototypes of postures. This reflects well the 16 postures that actually occurred in the experiment. In 91% of all cases an unambiguous assignment of a posture to exactly one generated prototype was achieved. On the other hand an unambiguous assignment of a prototype to a posture was obtained in 86%.
Tessendorf
Bernd
B
Wearable Computing Lab., ETH Zurich, Switzerland. tessendorf@ife.ee.ethz.ch
Arnrich
Bert
B
Schumm
Johannes
J
Setz
Cornelia
C
Troster
Gerhard
G
D056044:Actigraphy / Q000295:instrumentation*; D000328:Adult; D000465:Algorithms; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D008297:Male; D008365:Manometry / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011187:Posture / Q000502:physiology*; D011312:Pressure; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity