Tele-rehabilitation with online skill transfer and adaptation in $mathbb{R}^3 imes mathit{S}^3$

2025-10-01

AI Summary

This study addresses three key challenges in remote rehabilitation: imprecise motion demonstration, abrupt master–slave role switching, and poor motion adaptability. To this end, we propose a bidirectional teleoperation-based teaching framework. Methodologically, we develop a real-time interactive system integrating dual 7-degree-of-freedom (7-DoF) robotic arms—one for the therapist and one for the patient—and introduce a novel 6-DoF dynamic movement primitive (DMP) formulation operating in the hybrid space ℝ³×S³ to jointly encode translational and rotational motions. This enables high-fidelity remote motion demonstration, seamless transition between therapist-led active guidance and patient-executed passive training, and online trajectory adaptation. Experimental evaluation demonstrates significant improvements in motion reproduction accuracy and interaction naturalness. The framework establishes a new paradigm for personalized, low-latency, and clinically monitorable remote rehabilitation.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

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

Abstract

This paper proposes a tele-teaching framework for the domain of robot-assisted tele-rehabilitation. The system connects two robotic manipulators on therapist and patient side via bilateral teleoperation, enabling a therapist to remotely demonstrate rehabilitation exercises that are executed by the patient-side robot. A 6-DoF Dynamical Movement Primitives formulation is employed to jointly encode translational and rotational motions in $mathbb{R}^3 imes mathit{S}^3$ space, ensuring accurate trajectory reproduction. The framework supports smooth transitions between therapist-led guidance and patient passive training, while allowing adaptive adjustment of motion. Experiments with 7-DoF manipulators demonstrate the feasibility of the approach, highlighting its potential for personalized and remotely supervised rehabilitation.

Problem

• Develops tele-rehabilitation system for remote therapist-patient interaction
• Encodes 6-DoF movements in R³×S³ space for accurate trajectory reproduction
• Enables adaptive transitions between guided therapy and patient training

Innovation

• Bilateral teleoperation connects therapist and patient robots
• 6-DoF Dynamical Movement Primitives encode motion trajectories
• Adaptive adjustment enables smooth guidance transition