Researchers have found that two devices commonly used for video gaming and security systems are effective in detecting the early onset of illness and fall risks in elderly people.
Marjorie Skubic, professor of electrical and computer engineering at Missouri University’s (MU) College of Engineering, is working with doctoral student Erik Stone to use the Microsoft Kinect to monitor behaviour and routine changes in patients at TigerPlace, a residential home for senior citizens. These changes can reportedly indicate increased risk of falls or early symptoms of illnesses.
‘The Kinect uses infrared light to create a depth image that produces data in the form of a silhouette, instead of a video or photograph,’ said Stone. ’This alleviates many seniors’ concerns about privacy when traditional web camera-based monitoring systems are used.’
Another doctoral student, Liang Liu, is collaborating with Mihail Popescu, assistant professor in the College of Engineering and the Department of Health Management and Informatics in the MU School of Medicine, to develop a fall detection system that uses Doppler radar to recognise changes in walking, bending and other movements that may indicate a heightened risk for falls.
Different human body parts are said to create unique images, or ‘signatures’, on Doppler radar. Since falls combine a series of body part motions, the radar system can recognise a fall based on its distinct ‘signature’.
‘Falls are especially dangerous for older adults and if they don’t get help immediately, the chances of serious injury or death are increased,’ said Liu. ’If emergency personnel are informed about a fall right away, it can significantly improve the outcome for the injured patient.’
Both motion-sensing systems provide automated data that alert care providers when patients need assistance or a medical intervention.
The systems are currently used for monitoring residents at TigerPlace in Columbia. Skubic said the system allows residents to maintain their independence and take comfort in knowing that illnesses or falls may be detected early.