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Authors: Panagiotis Kostopoulos 1 ; Tiago Nunes 1 ; Kevin Salvi 1 ; Michel Deriaz 1 and Julien Torrent 2

Affiliations: 1 University of Geneva, Switzerland ; 2 Fondation Suisse pour les Téléthèses (FST), Switzerland

ISBN: 978-989-758-077-2

Keyword(s): Fall Detection, Smartwatch, Sensors, Residual Movement, Accelerometer, Alarm.

Related Ontology Subjects/Areas/Topics: Applications ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Health Engineering and Technology Applications ; Pattern Recognition ; Sensors and Early Vision ; Signal Processing ; Software Engineering

Abstract: Every year over 11 million falls are registered. Falls play a critical role in the deterioration of the health of the elderly and the subsequent need of care. This paper presents a fall detection system running on a smartwatch (F2D). Data from the accelerometer is collected, passing through an adaptive threshold-based algorithm which detects patterns corresponding to a fall. A decision module takes into account the residual movement of the user, matching a detected fall pattern to an actual fall. Unlike traditional systems which require a base station and an alarm central, F2D works completely independently. To the best of our knowledge, this is the first fall detection system which works on a smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner for the commercialization of our system. Taking advantage of their experience with the end users, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account residual movement to increase the fall detection accuracy and summarizes the architecture and the implementation of the fall detection system. (More)

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Paper citation in several formats:
Kostopoulos, P.; Nunes, T.; Salvi, K.; Deriaz, M. and Torrent, J. (2015). Increased Fall Detection Accuracy in an Accelerometer-based Algorithm Considering Residual Movement.In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 30-36. DOI: 10.5220/0005179100300036

@conference{icpram15,
author={Panagiotis Kostopoulos. and Tiago Nunes. and Kevin Salvi. and Michel Deriaz. and Julien Torrent.},
title={Increased Fall Detection Accuracy in an Accelerometer-based Algorithm Considering Residual Movement},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={30-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005179100300036},
isbn={978-989-758-077-2},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Increased Fall Detection Accuracy in an Accelerometer-based Algorithm Considering Residual Movement
SN - 978-989-758-077-2
AU - Kostopoulos, P.
AU - Nunes, T.
AU - Salvi, K.
AU - Deriaz, M.
AU - Torrent, J.
PY - 2015
SP - 30
EP - 36
DO - 10.5220/0005179100300036

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