A significant number of individuals face challenges with hand movement due to neurological conditions, aging, or injuries. Accessing physical therapy is often difficult due to a shortage of therapists and other barriers to care. To address this issue, many patients rely on self-directed rehabilitation programs that allow them to exercise at home. While these programs can be beneficial, they often lack professional guidance, increasing the risk of incorrect exercise execution or loss of motivation. This project will enhance the effectiveness and engagement of home rehabilitation by leveraging artificial intelligence (AI) and motion-sensing technology. Beyond improving rehabilitation tools, the project will provide opportunities for students to work at the intersection of engineering and healthcare. Students will gain hands-on experience developing innovative technologies, exploring entrepreneurship, and engaging in public outreach to raise awareness of AI-powered rehabilitation solutions. This CAREER proposal focuses on advancing adaptive, self-directed hand rehabilitation programs through three technical innovations. First, it will develop a computer vision-based recovery monitoring system that integrates motion sensing and muscle activity data to model and visualize hand recovery dynamics. These recovery models will serve as real-time feedback to patients, offering a detailed understanding of their progress. Second, it aims to explore the predictive power of physiologica