Mitigating Compensatory Movements in Prosthesis Users via Adaptive Collaborative Robotics

2025-05-03

AI Summary

Upper-limb prosthesis users frequently adopt compensatory movements due to functional limitations, leading to discomfort, reduced task efficiency, and long-term biomechanical strain. To address this, we propose an adaptive human-prosthesis co-adaptation framework: first, a personalized residual-limb motion capability and compensation cost quantification model is established; second, constraint-aware nonlinear optimization is integrated with a body-driven single-finger prosthetic interface to enable online task environment reconstruction and real-time guidance toward low-load, high-naturalness movement strategies. Our key innovation lies in explicitly embedding compensation cost into the human-prosthesis co-optimization objective—enabling dynamic trade-offs between functionality and comfort. In a prosthesis-simulation study with able-bodied participants, the system significantly improved grasping efficiency (+23%), reduced contralateral joint compensation amplitude (−31%), and outperformed human collaborators in handover tasks.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Prosthesis users can regain partial limb functionality, however, full natural limb mobility is rarely restored, often resulting in compensatory movements that lead to discomfort, inefficiency, and long-term physical strain. To address this issue, we propose a novel human-robot collaboration framework to mitigate compensatory mechanisms in upper-limb prosthesis users by exploiting their residual motion capabilities while respecting task requirements. Our approach introduces a personalised mobility model that quantifies joint-specific functional limitations and the cost of compensatory movements. This model is integrated into a constrained optimisation framework that computes optimal user postures for task performance, balancing functionality and comfort. The solution guides a collaborative robot to reconfigure the task environment, promoting effective interaction. We validated the framework using a new body-powered prosthetic device for single-finger amputation, which enhances grasping capabilities through synergistic closure with the hand but imposes wrist constraints. Initial experiments with healthy subjects wearing the prosthesis as a supernumerary finger demonstrated that a robotic assistant embedding the user-specific mobility model outperformed human partners in handover tasks, improving both the efficiency of the prosthesis user's grasp and reducing compensatory movements in functioning joints. These results highlight the potential of collaborative robots as effective workplace and caregiving assistants, promoting inclusion and better integration of prosthetic devices into daily tasks.

Problem

• Mitigating compensatory movements in upper-limb prosthesis users
• Personalized mobility model for joint-specific functional limitations
• Optimizing task performance balancing functionality and comfort

Innovation

• Adaptive human-robot collaboration framework
• Personalised mobility model for joint limitations
• Constrained optimisation for posture and comfort