COLUMBIA, Mo. The aging process often forces older adults to give up things they value and enjoy, including their independence and the ability to exercise and participate in physical activities. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology (CERT) are working to help older adults live better lives by developing and evaluating motion-capture technology that monitors the physical functioning of older adults while preserving their privacy.
"Frequent assessment of physical function is a key indicator for detecting initial decline of health in older adults," said Marge Skubic, director of CERT and associate professor of electrical and computer engineering in the MU College of Engineering. "The technology we are developing will help health care providers identify potential health problems, which provides a window of opportunity for interventions and treatments to alleviate the problems before they become worse."
Skubic recently received a $900,000 grant from the National Science Foundation to work with an interdisciplinary team of researchers at MU and the University of Washington to advance CERT's current projects, which include an exercise feedback system and a fall recognition system.
Using existing motion capture methods, CERT researchers developed an exercise feedback system to increase exercise effectiveness and safety for older adults. The automated system uses standard Web cams to capture the silhouette sequences of participants while they exercise, and provides feedback about posture and gait, including stride, balance and body position.
"This information will help older adults understand more about their posture and movement during exercise, and lead to improved effectiveness and safety of exercise regimens," said Greg Alexander, assistant professor in the MU Sinclair School of Nursing and lead researcher of the project.
In another project, CERT's researchers completed an evaluation of a video-based fall recognition system for elders. The system preserves privacy by extracting silhouettes acquired from multiple cameras viewing the same scene. The silhouettes are used to build a 3-D object, and the object's activity is analyzed by the system. The system generates summaries and distinguishes between fall and non-fall activities.
Using the results of these projects, the researchers will study vision-based detection methods designed to capture continuous and automated assessments of older adults' physical functioning in multiple-person environments.
|Contact: Emily Smith|
University of Missouri-Columbia