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

Sensors

Hȧkon Bryn¹  Martin Kucera¹  Simon Scheurer¹  Marcel Bärtschi¹  Tobias Meerstetter¹  Janina Koch¹  Tobias Nef²  Prabitha Urwyler² 

[1] Department of Engineering and Information Technology, Bern University of Applied Sciences, 3401 Burgdorf, Switzerland;Gerontechnology and Rehabilitation Group, University of Bern, 3008 Bern, Switzerland;

关键词: wearable;    fall detection;    healthcare;    sensors;    threshold algorithm;   

DOI  :  10.3390/s19061357

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown