Using the Kinect to help detect illness, prevent falls and help older adults
- Posted: Dec 02, 2011 at 6:00 AM
- 7,161 Views
- 5 Comments
Loading User Information from Channel 9
Something went wrong getting user information from Channel 9
Loading User Information from MSDN
Something went wrong getting user information from MSDN
Loading Visual Studio Achievements
Something went wrong getting the Visual Studio Achievements
The final post in our medical week inspiration series is how the Kinect can be used to provide 24x7 'eyes' to help those whom might need a little extra care...
COLUMBIA, Mo. –Many older adults lose their independence as their health declines and they are compelled to move into assisted care facilities. Researchers at the University of Missouri and TigerPlace, an independent living community, have been using motion-sensing technology to monitor changes in residents’ health for several years. Now, 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 risk in seniors.
Marjorie Skubic, professor of electrical and computer engineering in the MU College of Engineering, is working with doctoral student, Erik Stone, to use the Microsoft Kinect, a new motion-sensing camera generally used as a video gaming device, to monitor behavior and routine changes in patients at TigerPlace. These changes can indicate increased risk for 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 recognize changes in walking, bending and other movements that may indicate a heightened risk for falls. Different human body parts create unique images, or “signatures,” on Doppler radar. Since falls combine a series of body part motions, the radar system can recognize a fall based on its distinct “signature.”
Both motion-sensing systems provide automated data that alert care providers when patients need assistance or a medical intervention. The systems currently are used for monitoring residents at TigerPlace in Columbia. Skubic says the system allows residents to maintain their independence and take comfort in knowing that illnesses or falls may be detected early.
Stone’s study, “Evaluation of an Inexpensive Depth Camera for Passive In-Home Fall Risk Assessment,” won the best paper award at the Pervasive Health Conference, in Dublin, Ireland in May. Liu’s study, “Automatic Fall Detection Based on Doppler Radar Motion,” received the best poster award at the conference. Liu’s paper was a collaboration with GE Global Research and co-authored by Tarik Yardibi and Paul Cuddihy. TigerPlace is a joint project of the Sinclair School of Nursing and AmErikare, a long-term care company. For more information about MU’s interdisciplinary eldercare technology research, visit http://eldertech.missouri.edu or www.agingmo.com.
STAYING HOME AND STAYING SAFE
“The goal is to allow independence and what’s called, ‘aging in place,’” said Skubic.
“People want to stay in their own homes, and this sensor system helps them to do just that. They get the benefits of constant monitoring without the loss of independence and privacy.
“We don’t want them to have to do anything different,” said Skubic. “That’s why the system uses sensors mounted in the environment. Elderly people don’t want to have to wear sensors.”
And what happens if they get up to use the bathroom in the middle of the night? That’s when many falls occur, but people are unlikely to wear sensors to bed or put them on in the middle of the night when they get up, Skubic said.
The non-invasive system Skubic and her team of researchers are investigating uses four separate types of sensing to develop a computer model of how an individual moves about and then watches for changes. Changes in a person’s movement, such as shuffling or limping, could be warning signs that a person is at greater risk for a fall.
This is a big improvement from current systems that are more like “On-Star” for the home, said Skubic. They only allow health professionals to react to possible falls, not prevent them from happening.
“I think the real power is in noticing subtle changes,” Skubic said. “Fixing small health problems can avoid a catastrophic health event.”
By monitoring a person’s motion patterns and gait, then watching for changes in the patterns, the computerized system can look for warning signs that a person’s risk for falling has increased.
HOW IT WORKS:
1. Silhouette imager...
3. Acoustic System ...
4. Radar ...