Gerontology· 2014Q2
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
- 18,959citations
- Q2SCImago
- 2014year
Short summary
An accelerometry-based fall detection system achieved 80% sensitivity in detecting 12 out of 15 real-life falls among 16 elderly individuals over 15,500 hours, with an initial false alarm rate of 0.049 alarms per hour, reducible to 0.025 alarms per hour.
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Key points
- The fall detection system had an 80% sensitivity, detecting 12 of 15 real-life falls.
- The initial false alarm rate was 0.049 alarms per usage hour.
- Data analysis modification reduced the false alarm rate to 0.025 alarms per hour (1 false alarm per 40 hours).
- Data was collected from 16 elderly individuals over 15,500 hours.
AI-generated from the title and abstract; the full text is not read.
Abstract
BACKGROUND: About a third of home-dwelling older people fall each year, and institutionalized older people even report a two- or threefold higher rate for falling. Automatic fall detection systems have been developed to support the independent and secure living of the elderly. Even though good fall detection sensitivity and specificity in laboratory settings have been reported, knowledge about the sensitivity and specificity of these systems in real-life conditions is still lacking. OBJECTIVE: The aim of this study was to evaluate the long-term fall detection sensitivity and false alarm rate of a fall detection prototype in real-life use. METHODS: A total of 15,500 h of real-life data from 16 older people, including both fallers and nonfallers, were monitored using an accelerometry-based sensor system with an implemented fall detection algorithm. RESULTS: The fall detection system detected 12 out of 15 real-life falls, having a sensitivity of 80.0%, with a false alarm rate of 0.049 alarms per usage hour with the implemented real-time system. With minor modification of data analysis the false alarm rate was reduced to 0.025 false alarms per hour, equating to 1 false fall alarm per 40 usage hours. CONCLUSION: These data suggest that automatic accelerometric fall detection systems might offer a tool for improving safety among older people.
The authors' abstract, as published at the source. Gerontology, 2014 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science