Data Driven Functional Tele-Rehabilitation of the Hand with SoV Wearable Robots

Rehabilitation is a critical part of recovery from stroke, of which there are about 780,000 new incidences annually in the US alone. Combining therapy and assistive functionality into a wearable data-driven system promises to reduce costs and improve outcomes compared to more expensive traditional therapy.

We will create soV wearable robotic systems that can be worn at home for extended periods of time, to provide assistance with activities of daily living (ADLs) while simultaneously monitoring user activities with embedded sensors. Continuous use of these robotic systems will generate copious data, including limb state and environmental interactions. This large dataset will provide us with the opportunity to use data analysis techniques to automatically characterize user capabilities and deficiencies with improved accuracy and resolution compared to current clinical practice. Automated performance measurements also promise to increase therapist efficiency and patient motivation.

Team: Krzysztof Gajos, Conor Walsh, Margo Seltzer, Robert Howe, and Susan Fasoli