Multi-Objective Optimization of a Lower Limb Prosthesis for Metabolically Efficient Walking Assistance

Enhancing energy efficiency is pivotal for advancing robotic lower limb prostheses. However, the conventional control strategy in powered prostheses displays notable variance in energy economy across terrains during walking. This challenge could be managed by developing control optimization methods to effectively reduce the subject's metabolic cost and the device's energy expenditure, thereby improving the walking economy in diverse environments. In this study, we investigate a multi-objective optimization problem (MOP) for controlling above-knee prostheses, intending to reduce prosthetic power consumption and the subject's metabolic costs during walking activities. To bolster real-time performance in the optimization loop, we introduce a knee-guided evolutionary algorithm (KGEA) for MOP, which efficiently reduces exploration space and time complexity, thus enabling the practical implementation of a limited number of solutions in each iteration. The effectiveness of the proposed optimization-based strategy was evaluated through experiments involving two lower limb amputees engaging in slope, stair, and flat walking. Our results demonstrated that the optimization-based strategy significantly reduced the subjects' metabolic consumption, with an average reduction of 16.53% for subject 1 and 10.23% for subject 2 across different terrains, compared to using the prosthesis without the optimization strategy. This promising outcome marks a substantial advancement in the development of energy-efficient, multi-degree-of-freedom power prostheses.