2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
March 14, 2016 to March 19, 2016
Timo Sztyler , University of Mannheim, Mannheim, Germany
Heiner Stuckenschmidt , University of Mannheim, Mannheim, Germany
Human activity recognition using mobile device sensors is an active area of research in pervasive computing. In our work, we aim at implementing activity recognition approaches that are suitable for real life situations. This paper focuses on the problem of recognizing the on-body position of the mobile device which in a real world setting is not known a priori. We present a new real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different positions. Further, we introduce a device localization method that uses random forest classifiers to predict the device position based on acceleration data. We perform the most complete experiment in on-body device location that includes all relevant device positions for the recognition of a variety of different activities. We show that the method outperforms other approaches achieving an F-Measure of 89% across different positions. We also show that the detection of the device position consistently improves the result of activity recognition for common activities.
Sensors, Context, Acceleration, Feature extraction, Gravity, Biomedical monitoring, Performance evaluation
T. Sztyler and H. Stuckenschmidt, "On-body localization of wearable devices: An investigation of position-aware activity recognition," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-9.