Enhancing Multi-Task Motion Planning Based on Improved DMPs for Lower Limb Prostheses

Abstract

Achieving natural locomotion across diverse environments with prosthetic limbs remains a significant challenge for amputees. Intelligent prosthetics leverage motion planning techniques using phase variables to emulate natural gait aligned with human movement intentions. However, traditional phase variable-based planning, which utilizes geometric human motion models, often lacks robustness when encountering external disturbances. Additionally, models derived from human walking data can only approximate a limited set of discrete tasks, hindering the construction of a comprehensive model. In this study, we present an advanced prosthetic motion planning approach that integrates Dynamic Motion Primitives (DMPs) to ensure robust performance across multiple tasks. We demonstrate that DMPs with human-in-the-loop effectively simulate human joint movement trajectories under various task conditions. Furthermore, we introduce a novel Multi-Task Dynamic Motion Primitives with Singular Value Decomposition (DMPs-SVD) method, which incorporates multiple feature trajectory learning. This approach constructs a coherent task model using a limited dataset of typical human walking patterns, enabling joint motion planning across diverse task scenarios. Experimental results validate the viability and efficacy of the proposed human-in-loop DMPs and DMPs-SVD techniques in prosthetic applications.