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.
Note to Practitioners
—Lower Limb amputees using powered prostheses experience energy efficiency challenges due to limited battery life, particularly with multi-degree-of-freedom prostheses over varied terrains. Additionally, compared to passive alternatives, the increased weight of powered prosthetics poses a challenge in managing users’ metabolic costs, requiring suitable optimization strategies to offset the additional burden and optimize body economy during locomotion. The proposed KGEA-based multi-objective optimization method, designed explicitly for above-knee prostheses, targets the reduction of prosthetic power consumption and subject’s metabolic costs during walking activities. This innovation will likely benefit individuals with lower limb amputations, offering them improved mobility and comfort across various terrains. By addressing variance in varied terrains and inefficiencies in energy consumption caused by traditional control strategies, this work can assist practitioners in creating more effective and user-friendly prosthetic devices. Furthermore, we explore continuous refinement of the multi-objective optimization model and KGEA to accommodate a broader range of user-profiles and walking scenarios, ensuring its applicability in real-world situations.