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

2025-12-10

AI Summary

To address low adherence to home-based rehabilitation and limited clinical resource accessibility among stroke survivors, this study proposes a virtual reality (VR)-based avatar-guided system grounded in adaptive optimal control. Methodologically, we introduce a novel real-time, multi-objective control framework that integrates Hogan’s minimum-jerk model with a data-driven “capability index,” enabling personalized, human-in-the-loop motor guidance within VR. The system incorporates nonlinear optimal control, smoothness-based biomechanical assessment, and closed-loop interactive feedback. Experimental results demonstrate significant improvements in both home-training adherence and movement fidelity. The approach ensures interpretability, individual adaptability, and scalability for remote deployment. This work establishes a clinically viable, intelligent, home-centered intervention paradigm for post-stroke neurorehabilitation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Develops adaptive avatar control for VR-based motor rehabilitation at home.
• Addresses accessibility and continuity challenges in post-stroke therapy.
• Uses optimal control to balance patient guidance with movement freedom.

Innovation

• Adaptive optimal control for real-time avatar guidance
• Multi-objective balance between patient movement and ideal kinematics
• Data-driven ability index adjusting gains based on progress