Falls are a major problem for the elderly leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The fall risk and detection system includes pulse-Doppler radar, a Microsoft Kinect, and two web cameras. Stunt actors performed falls in apartments each month for 2 years and participants completed a series of monthly fall risk assessments (FRAs) using standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants spatial and temporal gait parameters were measured as they walked on a GAITRite mat. Results of validation analyses are presented that compared radar and Kinect generated gait variables to ground truth data (FRAs and GAITRite variables). All FRAs are highly correlated with the Kinect data including gait velocity, stride length and stride time. Radar velocity is correlated to all FRAs and highly correlated to most. Algorithms were developed for automated detection of falls and measuring fall risk. Real-time alerts of actual falls are being generated and sent to clinicians in the facility providing faster responses to urgent situations. The in-home fall risk assessment and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.
Rantz, M., Skubic, M., Abbott, C., Galambos, C., Popescu, M., Keller, J., Stone, E., Back, J., Miller, S.J., & Petroski, G.F. (2015). Automated in-home fall risk assessment and detection sensor system for elders. The Gerontologist, 55(S1), S78-S87.