title: Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.affiliation: Sinclair School of Nursing and Family and Community Medicine, University of Missouri, Columbia. rantzm@missouri.edu.
authors.forename: Marilyn
authors.initials: M
authors.lastname: Rantz
authors.affiliation: Electrical and Computer Engineering, University of Missouri, Columbia.
authors.forename: Marjorie
authors.initials: M
authors.lastname: Skubic
authors.affiliation: School of Health Professions, Physical Therapy, University of Missouri, Columbia.
authors.forename: Carmen
authors.initials: C
authors.lastname: Abbott
authors.affiliation: School of Social Work, University of Missouri, Columbia.
authors.forename: Colleen
authors.initials: C
authors.lastname: Galambos
authors.affiliation: Health Management and Informatics, School of Medicine, University of Missouri, Columbia.
authors.forename: Mihail
authors.initials: M
authors.lastname: Popescu
authors.affiliation: Electrical and Computer Engineering, University of Missouri, Columbia.
authors.forename: James
authors.initials: J
authors.lastname: Keller
authors.affiliation: Center for Eldercare and Rehabilitation Technology, University of Missouri, Columbia.
authors.forename: Erik
authors.initials: E
authors.lastname: Stone
authors.affiliation: TigerPlace, Sinclair School of Nursing, University of Missouri, Columbia.
authors.forename: Jessie
authors.initials: J
authors.lastname: Back
authors.affiliation: Sinclair School of Nursing, University of Missouri, Columbia.
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Miller
authors.affiliation: Biostatistics and Research Design Unit, School of Medicine, University of Missouri, Columbia.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Petroski
mesh_terms: D000058:Accidental Falls; D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D000465:Algorithms; D005260:Female; D005684:Gait; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D018570:Risk Assessment; D012449:Safety; D012637:Security Measures; D014741:Video Recording
keywords: Automated algorithms; Fall detection; Fall risk; Falls