Fall detection and early medical response are challenging and promising aspects of home healthcare for older adults. A two-step algorithm for falls analyzed accelerometer data for 750 test events and found significance limits for body trunk angle change as well as falls. Automated detection of falls based upon motion sensing and fuzzy logic can be based upon evidence-derived rules.
More than one third of community-dwelling older adults and up to 60% of nursing home residents fall each year, with 10-15% of fallers sustaining a serious injury. Reliable automated fall detection can increase confidence in people with fear of falling, promote active safe living for older adults, and reduce complications from falls. The performance of a 2-stage fall detection algorithm using impact magnitudes and changes in trunk angles derived from user-based motion sensors was evaluated under laboratory conditions. Ten healthy participants were instrumented on the front and side of the trunk with 3D accelerometers. Participants simulated 9 fall conditions and 6 common activities of daily living. Fall conditions were simulated on a protective mattress. The experimental data set comprised 750 events (45 fall events and 30 nonfall events per participant) that were classified by the fall detection algorithm as either a fall or a nonfall using inputs from 3D accelerometers. Significant differences for impacts recorded, trunk angle changes (p < 0.01), and detection performances (p < 0.05) were found between fall and nonfall conditions. The proposed algorithm detected fall events during simulated fall conditions with a success rate of 93% and a false-positive rate of 29% during nonfall conditions. Despite a slightly superior identification performance for the accelerometer located on the front of the trunk, no significant differences were found between the two motion sensor locations. Automated detection of fall events based on user-based motion sensing and fuzzy logic shows promising results. Additional rules and optimization of the algorithm will be needed to decrease the false-positive rate.
Boissy, Patrick, Stéphane Choquette, Mathieu Hamel, and Norbert Noury. "User-based motion sensing and fuzzy logic for automated fall detection in older adults." Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association 13, no. 6 (December 2007): 683-693.