Hips do lie! A position-aware mobile fall detection system


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



DOI: tba
URL: https://www.ieee.org/conferences_events/conference...
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: 2018 IEEE International Conference on Pervasive Computing and Communications : PerCom 2018, Athens, Greece, March 19-23, 2018
Page range: 1-10
Conference title: PerCom 2018
Location of the conference venue: Athens, Greece
Date of the conference: 19.-23.03.2018
Place of publication: Piscataway, NJ
Publishing house: IEEE Computer Society
ISBN: tba
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
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. In this work, we propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Therefore, we combine sensor data of four publicly available datasets, covering about 100 subjects, 5 devices, and 3 sensor placements. In a comprehensive evaluation, 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.

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