Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults
by
Simon Scheurer 1,2, Janina Koch 1,2
, Martin Kucera 1, Hȧkon Bryn 1, Marcel Bärtschi 1, Tobias Meerstetter 1,2, Tobias Nef 3,4 and Prabitha Urwyler 3,4,5,*
Simon Scheurer 1,2, Janina Koch 1,2, Martin Kucera 1, Hȧkon Bryn 1, Marcel Bärtschi 1, Tobias Meerstetter 1,2, Tobias Nef 3,4 and Prabitha Urwyler 3,4,5,*
1
Department of Engineering and Information Technology, Bern University of Applied Sciences, 3401 Burgdorf, Switzerland
2
Oxomed AG, 3097 Liebefeld, Switzerland
3
Gerontechnology and Rehabilitation Group, University of Bern, 3008 Bern, Switzerland
4
ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland
5
University Neurorehabilitation Unit, Department of Neurology, University Hospital Inselspital, 3010 Bern, Switzerland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(6), 1357; https://doi.org/10.3390/s19061357
Received: 11 February 2019 / Revised: 11 March 2019 / Accepted: 11 March 2019 / Published: 18 March 2019
(This article belongs to the Section Physical Sensors)
Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.
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Keywords:
wearable; fall detection; healthcare; sensors; threshold algorithm
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MDPI and ACS Style
Scheurer, S.; Koch, J.; Kucera, M.; Bryn, H.; Bärtschi, M.; Meerstetter, T.; Nef, T.; Urwyler, P. Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults. Sensors 2019, 19, 1357.
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