An intelligent emergency response system: preliminary development and testing of automated fall detection.
We have designed an intelligent emergency response system to detect falls in the home. It uses image-based sensors. A pilot study was conducted using 21 subjects to evaluate the efficacy and performance of the fall-detection component of the system. Trials were conducted in a mock-up bedroom setting, with a bed, a chair and other typical bedroom furnishings. A small digital videocamera was installed in the ceiling at a height of approximately 2.6 m. The digital camera covered an area of approximately 5.0 m x 3.8 m. The subjects were asked to assume a series of postures, namely walking/standing, sitting/lying down in an inactive zone, stooping, lying down in a 'stretched' position, and lying down in a 'tucked' position. These five scenarios were repeated three times by each subject in a random order. These test positions totalled 315 tasks with 126 fall-simulated tasks and 189 non-fall-simulated tasks. The system detected a fall on 77% of occasions and missed a fall on 23%. False alarms occurred on only 5% of occasions. The results encourage the potential use of a vision-based system to provide safety and security in the homes of the elderly.
Lee
Tracy
T
Intelligent Assistive Technology and Systems Laboratory, Department of Occupational Therapy, University of Toronto, Canada.
Mihailidis
Alex
A
D000058:Accidental Falls; D000061:Accidents, Home; D000368:Aged; D004635:Emergency Medicine / Q000379:methods; D005260:Female; D016330:Frail Elderly; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D010865:Pilot Projects