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As you may know for a patient to rehabilitate following injury (surgical or non-surgical), the patient will be assigned to physical therapy (PT) clinics to attend a series of PT sessions. Besides the time spent on these sessions, the cost is also considerable to the patient. Some of these expenses are the actual costs associated with the delivery of physical therapy, transportation, and any fees the patient pays for missed PT sessions.

The GonioSense platform recreates the patient’s “in-clinic” experience by taking it to their home. GonioSense has the ability to tell you what exercise patient is engaged in. The patient can do PT supervised therapy sessions from home and on vacation. The clinician can create personalized programs and provide real-time protocol updates for their patients through the platform. The platform also allows clinicians to monitor patients’ daily progress. What’s more, the platform brings extra flexibility for an employee who has been released to work on “light duty” because they can do rehab at home and have therapist “visit” them after hours at home. Furthermore, there are cost savings to be realized by eliminating transportation costs to PT clinics as well as eliminating costs associated with missed PT sessions.

The GonioSense platform is consists of a mobile app and a web management portal. The mobile app leverages motion sensors built-in wearable devices (e.g. smart watch) and edge computing resources (e.g. ParaDrop) to detect and monitor rehabilitation exercises. The web management portal allows both patients and doctors to track progress. Through the web portal, patients can “attend” the therapy sessions assigned to them and track the progress they made every day. Clinicians can evaluate the rehabilitation status of each patient based on the achievements that the patient made and are able to make changes to the protocols if necessary.

Team Information

WiNGs Lab: https://wings.cs.wisc.edu/

Contact:
Prof. Suman Banerjee at University of Wisconsin Madison ([email protected])
Bozhao Qi at University of Wisconsin Madison ([email protected] or [email protected])

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