Penn State researchers are investigating the use of micro-doppler radars for use in clinical settings to predict injury risk and track recovery progress. The ability to detect subtle differences in human movement would enable health care workers to more accurately identify individuals who may be at risk of injury or to track progress precisely while individuals are recovering from an injury. College of Engineering and College of Medicine researchers teamed up to develop an accurate, reliable and cost-effective radar in front of which athlete study-subjects could jump.
“My students and I designed and constructed the radar system to characterize the micro-Doppler features of human gait, developed and tested various classification algorithms to separate patterns from different gait types and validated our hypothesis using measured data from athletes mimicking different gait patterns,” said Ram Narayanan, professor of electrical engineering in the School of Electrical Engineering and Computer Science.
Relying on the Doppler effect the radar provides precise information about the movements of the target, in this case, the athlete. This radar system could be a cost-effective, portable and scalable alternative to motion capture systems, which are currently the most accurate system for showing subtle movements. However, they are too expensive, large and time-intensive with use to be a viable option in most situations.
“The micro-Doppler radar has not been used in health care to this point and is a novel way to look at human movement,” said Dr. Cayce Onks, associate professor of family and community medicine and of orthopedics and rehabilitation in the College of Medicine, and physician at Penn State Health.
The study had NCAA athletes jump in front of the radar barefoot, wearing shoes, and wearing shoes with a heel lift. The radar was able to classify the jumps into each of those three categories with greater than 90% accuracy, something that existing motion-capture systems cannot accomplish, according to Onks.
“The findings of our study show that the micro-Doppler radar is able to ‘see’ differences in human movement that the human eye is not able to differentiate,” Onks said. “This type of information has the potential to be applied to hundreds of clinical applications, including but not limited to prevention of falls and disabilities, early detection of Parkinson’s, early detection of dementia, concussion diagnosis and identification of movement patterns that place individuals at risk for any number of musculoskeletal injuries, such as ankle injuries and ACL tears. Other applications may include determining readiness of an individual to return to movement following rehabilitation from an injury or surgery.”
The results were published in the journal Gait and Posture.
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