Soft fall detection using machine learning in wearable devices

Genoud, Dominique (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Cuendet, Vincent (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Torrent, Julien (Fondation Suisse pour les Teletheses, Neuchatel, Switzerland)

Wearable watches provide very useful linear acceleration information that can be use to detect falls. Howeverfalls not from a standing position are difficult to spot amongother normal activities. This paper describes methods, basedon pattern recognition using machine learning, to improve thedetection of "soft falls". The values of the linear accelerometersare combined in a robust vector that will be presented as inputto the algorithms. The performance of these different machinelearning algorithms is discussed and then, based on the bestscoring method, the size of the time window fed to the systemis studied. The best experiments lead to results showing morethan 0.9 AUC on a real dataset. In a second part, a prototypeimplementation on an Android platform using the best resultsobtained during the experiments is described.


Mots-clés:
Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
Crans-Montana, Suisse, 23-25 March 2016
Date:
Crans-Montana, Suisse
23-25 March 2016
2016
Pagination:
5 p.
Publié dans
Proceedings of the 30th IEEE International Conference on Advanced Information Networking and Applications (AINA) 2016
DOI:
ISSN:
1550-445X
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 Notice créée le 2016-09-28, modifiée le 2018-08-31

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