Marilyn Rantz1, Marjorie Skubic2, Carmen Abbott3, Colleen Galambos4, Mihail Popescu5, James Keller2, Erik Stone6, Jessie Back7, Steven J Miller8, Gregory F Petroski9. 1. Sinclair School of Nursing and Family and Community Medicine, University of Missouri, Columbia. rantzm@missouri.edu. 2. Electrical and Computer Engineering, University of Missouri, Columbia. 3. School of Health Professions, Physical Therapy, University of Missouri, Columbia. 4. School of Social Work, University of Missouri, Columbia. 5. Health Management and Informatics, School of Medicine, University of Missouri, Columbia. 6. Center for Eldercare and Rehabilitation Technology, University of Missouri, Columbia. 7. TigerPlace, Sinclair School of Nursing, University of Missouri, Columbia. 8. Sinclair School of Nursing, University of Missouri, Columbia. 9. Biostatistics and Research Design Unit, School of Medicine, University of Missouri, Columbia.
Abstract
PURPOSE OF THE STUDY: Falls are a major problem for the elderly people 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. DESIGN AND METHODS: A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables. RESULTS: All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05) to all the FRAs and highly correlated (p < .01) to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations. IMPLICATIONS: The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.
PURPOSE OF THE STUDY: Falls are a major problem for the elderly people 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. DESIGN AND METHODS: A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables. RESULTS: All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05) to all the FRAs and highly correlated (p < .01) to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations. IMPLICATIONS: The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.
Authors: K L Perell; A Nelson; R L Goldman; S L Luther; N Prieto-Lewis; L Z Rubenstein Journal: J Gerontol A Biol Sci Med Sci Date: 2001-12 Impact factor: 6.053
Authors: Marilyn Rantz; Lorraine J Phillips; Colleen Galambos; Kari Lane; Gregory L Alexander; Laurel Despins; Richelle J Koopman; Marjorie Skubic; Lanis Hicks; Steven Miller; Andy Craver; Bradford H Harris; Chelsea B Deroche Journal: J Am Med Dir Assoc Date: 2017-07-12 Impact factor: 4.669
Authors: Cameron J Gettel; Kelsey Hayes; Renee R Shield; Kate M Guthrie; Elizabeth M Goldberg Journal: Acad Emerg Med Date: 2020-03-15 Impact factor: 3.451