Capturing habitual, in-home gait parameter trends using an inexpensive depth camera.
Results are presented for measuring the gait parameters of walking speed, stride time, and stride length of five older adults continuously, in their homes, over a four month period. The gait parameters were measured passively, using an inexpensive, environmentally mounted depth camera, the Microsoft Kinect. Research has indicated the importance of measuring a person's gait for a variety of purposes from fall risk assessment to early detection of health problems such as cognitive impairment. However, such assessments are often done infrequently and most current technologies are not suitable for continuous, long term use. For this work, a single Microsoft Kinect sensor was deployed in four apartments, containing a total of five residents. A methodology for generating trends in walking speed, stride time, and stride length based on data from identified walking sequences in the home is presented, along with trend estimates for the five participants who were monitored for this work.
Center for Eldercare and Rehabilitation, Technology, Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA. ees6c6@mizzou.edu
Erik E
EE
Stone
Marjorie
M
Skubic
D000058:Accidental Falls / Q000517:prevention & control*; D056044:Actigraphy / Q000295:instrumentation*; D000368:Aged; D000369:Aged, 80 and over; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D017143:Fuzzy Logic; D005684:Gait / Q000502:physiology*; D015577:Geriatric Assessment / Q000379:methods; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation*; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010355:Patient Identification Systems / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D018910:Video Games