An Anthropometry-Based Personalization of Powered Knee Prosthesis for Metabolic Efficiency

Abstract

Traditional tuning methods of assistance parameters rely on the experience of human experts but often fail to achieve optimal performance. Human-in-the-loop optimization improves parameter selection but requires extensive in-lab testing. In this study, we rigorously tested two control parameters, early stance knee flexion angle (5° to 12°) and swing initiation timing (55% to 65% of the gait cycle), with ten individuals with transfemoral amputation using a commercially available robotic prosthetic knee, Össur Power Knee, and a passive foot, Pro-Flex LP. We measured energy expenditure, joint work, and user preferences during treadmill walking. Results showed a 15.6% reduction in metabolic rate with stance flexion decreasing from 12° to 5° (p<0.05). User preferences favored lower stance flexion and personalized swing initiation. Personalized-best settings reduced the metabolic rate by 4.1% (stance flexion) and 9.8% (swing initiation) compared to the best-on-average settings (p<0.05). These reductions were also significant when compared to the device default and clinically tuned settings (p<0.05). We proposed an offline learning approach using anthropometric, gait, and prosthesis-related data to estimate optimal settings, yielding a 7.1% reduction in metabolic rate (p<0.05). Our results suggest that this approach achieves comparable energy efficiency without lengthy experiments, enabling automatic parameter tuning with initial measurements.