NSF CAREER Project

Enhancing Self-Directed Hand Rehabilitation with AI-Driven Recovery Dynamics Monitoring and Motivation Boosting

The HiMER Lab’s NSF CAREER project develops AI-enabled methods to support self-directed hand rehabilitation. The project focuses on monitoring recovery dynamics and designing motivational support that helps people stay engaged, consistent, and informed throughout the rehabilitation process.

This work brings together human factors, intelligent interfaces, and health-centered technology to address a central question: how can AI help people rehabilitate more effectively outside the clinic while preserving autonomy, engagement, and trust?

Why this project matters

Hand rehabilitation often requires long-term, repetitive practice. Many patients struggle to maintain motivation, follow exercise routines, or understand whether their recovery is progressing as expected. These challenges can reduce adherence and limit outcomes.

This project aims to improve self-directed rehabilitation by studying how AI systems can:

• monitor recovery-related movement and performance patterns
• provide meaningful feedback on progress
• sustain motivation during repetitive exercises
• support users without creating unnecessary burden or dependence

Research approach

The project investigates AI-assisted rehabilitation support through a combination of sensing, modeling, and human-centered interaction design. The research examines how people respond to AI-generated recovery feedback and motivational support during self-directed hand rehabilitation activities.

The work includes:

• monitoring recovery dynamics from user interaction and performance data
• designing AI-based feedback and motivational strategies
• evaluating usability, engagement, and perceived usefulness
• studying how support should adapt to different recovery stages and user needs

Broader goals

This project advances the design of AI systems that support health behavior and rehabilitation in a way that is personalized, practical, and grounded in human needs. The goal is not just to automate feedback, but to help people stay active participants in their own recovery.

The project is aligned with the principles of human-centered design, accessibility, and responsible AI.

Expected impact

This research is expected to contribute to:

• new knowledge on AI-supported rehabilitation behavior
• design principles for motivational recovery systems
• better understanding of how users interpret progress feedback
• improved support for self-directed hand therapy and recovery

Project updates

Updates, publications, prototypes, and student contributions related to this award will be shared here as the project progresses.

Contact

For questions about this project or collaboration opportunities, please contact the PI.