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
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.
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