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/...
Weitere URL:	https://www.researchgate.net/publication/333464958...
Dokumenttyp:	Zeitschriftenartikel
Erscheinungsjahr:	2019
Titel einer Zeitschrift oder einer Reihe:	Pervasive and Mobile Computing
Band/Volume:	58
Seitenbereich:	Article 101026
Ort der Veröffentlichung:	Amsterdam [u.a.]
Verlag:	Elsevier
ISSN:	1574-1192
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-) Fakultät für Betriebswirtschaftslehre > Wirtschaftsinformatik II (Becker 2006-2021)
Fachgebiet:	004 Informatik
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.