A Wireless Sensor-Based System for Self-tracking Activity Levels Among Manual Wheelchair Users

Alexandre Grillon; Andres Perez-Uribe; Hector Satizabal; Laurent Gantel; David Silva Andrade; Andres Upegui; Francis Degache

eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers

Research Article

A Wireless Sensor-Based System for Self-tracking Activity Levels Among Manual Wheelchair Users

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@INPROCEEDINGS{10.1007/978-3-319-49655-9_31,
    author={Alexandre Grillon and Andres Perez-Uribe and Hector Satizabal and Laurent Gantel and David Silva Andrade and Andres Upegui and Francis Degache},
    title={A Wireless Sensor-Based System for Self-tracking Activity Levels Among Manual Wheelchair Users},
    proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers},
    proceedings_a={EHEALTH360},
    year={2017},
    month={1},
    keywords={Self-tracking Wheelchair Handicap Wireless sensors Wearables Machine learning},
    doi={10.1007/978-3-319-49655-9_31}
}

Alexandre Grillon
Andres Perez-Uribe
Hector Satizabal
Laurent Gantel
David Silva Andrade
Andres Upegui
Francis Degache
Year: 2017
A Wireless Sensor-Based System for Self-tracking Activity Levels Among Manual Wheelchair Users
EHEALTH360
Springer
DOI: 10.1007/978-3-319-49655-9_31

Alexandre Grillon^,*, Andres Perez-Uribe^,*, Hector Satizabal^,*, Laurent Gantel^,*, David Silva Andrade^,*, Andres Upegui^,*, Francis Degache^,*

*Contact email: alexandre.grillon@heig-vd.ch, andres.perez-uribe@heig-vd.ch, hector-fabio.satizabal-mejia@heig-vd.ch, laurent.gantel@hesge.ch, david.dasilva-andrade@hesge.ch, andres.upegui@hesge.ch, francis.degache@hesav.ch

Abstract

ActiDote —activity as an antidote— is a system for manual wheelchair users that uses wireless sensors to recognize activities of various intensity levels in order to allow self-tracking while providing motivation. In this paper, we describe both the hardware setup and the software pipeline that enable our system to operate. Laboratory tests using multi-modal fusion and machine learning reveal promising results attaining a F1-score classification performance of 0.97 on five different wheelchair-based activities belonging to four intensity levels. Finally, we show that such a low cost system can be used for an easy self-monitoring of physical activity levels among manual wheelchair users.

Keywords: Self-tracking Wheelchair Handicap Wireless sensors Wearables Machine learning

Published: 2017-01-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-49655-9_31