683 TELEMEDICINE AND e-HEALTH Volume 13, Number 6, 2007 © Mary Ann Liebert, Inc. DOI: 10.1089/tmj.2007.0007 Original Research User-Based Motion Sensing and Fuzzy Logic for Automated Fall Detection in Older Adults PATRICK BOISSY, Ph.D., 1,2 STÉPHANE CHOQUETTE, B.Sc., 1,2 MATHIEU HAMEL, M.Sc., 1 and NORBERT NOURY, Ph.D. 1,3 ABSTRACT More than one third of community-dwelling older adults and up to 60% of nursing home res- idents 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 liv- ing 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 sim- ulated 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 im- pacts 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 dur- ing simulated fall conditions with a success rate of 93% and a false-positive rate of 29% dur- ing nonfall conditions. Despite a slightly superior identification performance for the ac- celerometer 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 mo- tion sensing and fuzzy logic shows promising results. Additional rules and optimization of the algorithm will be needed to decrease the false-positive rate. 1 Research Centre on Aging, Sherbrooke Geriatric University Institute, Sherbrooke, Quebec. 2 Department of Kinesiology, FEPS, Université de Sherbrooke, Sherbrooke, Quebec. 3 TIMC-IMAG, Université Joseph Fourier, Grenoble, France. INTRODUCTION F ALLS ARE A MAJOR PUBLIC HEALTH CONCERN and one of the greatest obstacles to inde- pendent living for older adults. More than one third of community-dwelling older adults and up to 60% of nursing home residents fall each year. 1 The incidence of falls rises steadily with advancing age and gets even worse among nursing home residents, where multiple falls