Beyond position-awareness - extending a self-adaptive fall detection system

Krupitzer, Christian ; Sztyler, Timo ; Edinger, Janick ; Breitbach, Martin ; Stuckenschmidt, Heiner ; Becker, Christian

DOI:	https://doi.org/10.1016/j.pmcj.2019.05.007
URL:	https://www.sciencedirect.com/science/article/abs/...
Additional URL:	https://www.researchgate.net/publication/333464958...
Document Type:	Article
Year of publication:	2019
The title of a journal, publication series:	Pervasive and Mobile Computing
Volume:	58
Page range:	Article 101026
Place of publication:	Amsterdam [u.a.]
Publishing house:	Elsevier
ISSN:	1574-1192
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-) Business School > Wirtschaftsinformatik II (Becker)
Subject:	004 Computer science, internet
Abstract:	Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. We propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Using the data of four publicly available datasets, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor's position at runtime. In this paper, we extend our self-adaptive fall detection system with (i) additional algorithms for fall detection, (ii) an approach for cross-positional sensor fusion, (iii) a fall detection approach that relies on outlier detection, and (iv) a smart fall alert. Additionally, we present implementation and evaluation of these extensions.