Adaptive Optimal Control for Avatar-Guided Motor Rehabilitation in Virtual Reality

TL;DR

This paper introduces an adaptive, avatar-guided virtual reality system for personalized motor rehabilitation, utilizing optimal control and real-time patient performance data to enhance remote stroke therapy.

Contribution

It presents a novel control-theoretic framework enabling real-time, adaptive avatar guidance for at-home stroke rehabilitation using interpretable optimal control strategies.

Findings

System enables personalized therapy at home with clinician monitoring.

Simulation and preliminary trials show effective adaptation to patient progress.

Potential for scalable, engaging remote physiotherapy using virtual reality.

Abstract

A control-theoretic framework for autonomous avatar-guided rehabilitation in virtual reality, based on interpretable, adaptive motor guidance through optimal control, is presented. The framework faces critical challenges in motor rehabilitation due to accessibility, cost, and continuity of care, with over 50% of patients inability to attend regular clinic sessions. The system enables post-stroke patients to undergo personalized therapy in immersive virtual reality at home, while being monitored by clinicians. The core is a nonlinear, human-in-the-loop control strategy, where the avatar adapts in real time to the patient's performance. Balance between following the patient's movements and guiding them to ideal kinematic profiles based on the Hogan minimum-jerk model is achieved through multi-objective optimal control. A data-driven "ability index" uses smoothness metrics to dynamically adjust control gains according to the patient's progress. The system was validated through simulations and preliminary trials, and shows potential for delivering adaptive, engaging and scalable remote physiotherapy guided by interpretable control-theoretic principles.