A First-of-Its-Kind Approach to Stroke Rehabilitation ScenicMR is not just another medical app—it is an intelligent, adaptable rehabilitation system designed to provide personalized therapy to patients from the comfort of their homes. By using AR headsets like the Meta Quest, ScenicMR allows patients to follow interactive, AI-assisted therapy sessions while receiving real-time guidance and feedback. “We have completed development of our prototype AR app based on our ScenicMR technology and are about to initiate a clinical study at UC San Francisco involving patients with stroke,” explains Sanjit Seshia, Ph.D., lead principal investigator (PI) on the project, Cadence Founders Chair and professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. “In addition to evaluating our system, we will conduct interviews with patients to better understand their challenges with at-home rehabilitation and use their insights to inform the next version of our ScenicMR system.”
Stock images: AI and stroke rehabilitation
Bridging the Gap Between Clinicians and Technology One of the biggest barriers to adopting AR-based therapy is the difficulty in customizing rehabilitation exercises for each patient’s unique physical deficits and goals, and then implementing them in a home environment. Traditionally, this would require skilled programmers to design individualized therapy programs—an impractical and costly approach. However, the ScenicMR project solves this challenge through a groundbreaking innovation: clinicians could create and modify patient-specific rehabilitation programs without any programming knowledge. Using the Scenic probabilistic programming language, created at UC Berkeley in Professor Seshia’s group, along with generative AI, clinicians would customize rehabilitation exercises tailored to a patient’s mobility, progress and needs —ensuring that each therapy session remains both engaging and clinically effective. “This project can transform the delivery of effective, innovative and personalized health interventions, especially for those who live in rural areas with limited access to care or those with socio-economic limitations,” UCSF co-PI Cathra Halabi, M.D., said. Professor Yasser Shoukry, Ph.D., co-PI of the project from UC Irvine, added, “We are collaboratively developing a framework to automate data collection, analysis and decision-making in home-based rehabilitation while ensuring compliance with clinical practice and privacy constraints.”
38 Impact Report 2023 - 24 | UC NI
Scenic MR Team, image courtesy of Sanjit Seshia
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