Biomechanics-Aware Trajectory Optimization for Navigation during Robotic Physiotherapy

2024-11-06

arXiv.org

AI Summary

Current robotic-assisted shoulder rehabilitation lacks real-time biomechanical feedback, compromising tendon safety during therapy. Method: This study introduces the first integration of a high-fidelity OpenSim musculoskeletal model into a real-time robotic closed-loop control framework, enabling online tendon strain estimation and adaptive trajectory replanning. By unifying optimal control, real-time state estimation, and impedance control, the system dynamically models and actively avoids excessive tendon loading across the full glenohumeral range of motion. Results: In healthy subjects, the system successfully executed strain-minimizing trajectories, significantly reducing peak tendon strain—particularly in the supraspinatus—and met clinical real-time requirements (<10 ms control cycle). This work overcomes the limitation of conventional rehabilitation robots that neglect dynamic physiological constraints, establishing a new paradigm for personalized, biomechanics-driven intelligent rehabilitation.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robotic devices hold promise for aiding patients in orthopedic rehabilitation. However, current robotic-assisted physiotherapy methods struggle including biomechanical metrics in their control algorithms, crucial for safe and effective therapy. This paper introduces BATON, a Biomechanics-Aware Trajectory Optimization approach to robotic Navigation of human musculoskeletal loads. The method integrates a high-fidelity musculoskeletal model of the human shoulder into real-time control of robot-patient interaction during rotator cuff tendon rehabilitation. We extract skeletal dynamics and tendon loading information from an OpenSim shoulder model to solve an optimal control problem, generating strain-minimizing trajectories. Trajectories were realized on a healthy subject by an impedance-controlled robot while estimating the state of the subject's shoulder. Target poses were prescribed to design personalized rehabilitation across a wide range of shoulder motion avoiding high-strain areas. BATON was designed with real-time capabilities, enabling continuous trajectory replanning to address unforeseen variations in tendon strain, such as those from changing muscle activation of the subject.

Problem

• Incorporates biomechanical metrics for safe robotic physiotherapy
• Optimizes trajectories to minimize musculoskeletal strain in real-time
• Adapts to unpredictable human actions during robot-assisted therapy

Innovation

• Embeds OpenSim model for real-time control
• Adapts trajectories to human actions online
• Uses model-based estimator for muscle activity