Position-aware activity recognition with wearable devices

Sztyler, Timo ; Stuckenschmidt, Heiner ; Petrich, Wolfgang

DOI: https://doi.org/10.1016/j.pmcj.2017.01.008
URL: http://www.sciencedirect.com/science/article/pii/S...
Additional URL: http://publications.wim.uni-mannheim.de/informatik...
Document Type: Article
Year of publication: 2017
The title of a journal, publication series: Pervasive and Mobile Computing
Volume: 38
Issue number: 2
Page range: 281-295
Place of publication: Amsterdam
Publishing house: Elsevier
ISSN: 1574-1192
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Reliable human activity recognition with wearable devices enables the development of human-centric pervasive applications. We aim to develop a robust wearable-based activity recognition system for real life situations where the device position is up to the user or where a user is unable to collect initial training data. Consequently, in this work we focus on the problem of recognizing the on-body position of the wearable device ensued by comprehensive experiments concerning subject-specific and cross-subjects activity recognition approaches that rely on acceleration data. We introduce a device localization method that predicts the on-body position with an F-measure of 89% and a cross-subjects activity recognition approach that considers common physical characteristics. In this context, we present a real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different on-body positions. Our results show that the detection of the device position consistently improves the result of activity recognition for common activities. Regarding cross-subjects models, we identified the waist as the most suitable device location at which the acceleration patterns for the same activity across several people are most similar. In this context, our results provide evidence for the reliability of physical characteristics based cross-subjects models.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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