Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Scheurer, Simon; Koch, Janina; Kucera, Martin; Bryn, Hȧkon; Bärtschi, Marcel; Meerstetter, Tobias; Nef, Tobias; Urwyler, Prabitha (2019). Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults. Sensors, 19(6) Molecular Diversity Preservation International MDPI 10.3390/s19061357

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Gerontechnology and Rehabilitation
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Nef, Tobias and Urwyler-Harischandra, Prabitha

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1424-8220

Publisher:

Molecular Diversity Preservation International MDPI

Language:

English

Submitter:

Angela Amira Botros

Date Deposited:

27 Jun 2019 13:12

Last Modified:

22 Oct 2019 21:29

Publisher DOI:

10.3390/s19061357

PubMed ID:

30889925

BORIS DOI:

10.7892/boris.130181

URI:

https://boris.unibe.ch/id/eprint/130181

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