Efficient and Scalable Tuning of Continuous Impedance Control for Powered Knee Prostheses.

Powered knee prostheses based on continuous impedance control enable smooth and human-like joint behavior; however, practical tuning remains challenging due to the high dimensionality of impedance functions and subject-dependent variability. In this work, we propose a structured low-dimensional tuning framework in which continuous stiffness, damping, and equilibrium-angle functions are first identified offline from able-bodied walking data using kinematics-informed convex optimization and then parameterized through principal component analysis (PCA). The resulting PC weights serve as interpretable tuning variables, enabling systematic and scalable adjustment of prosthetic knee behavior. Sensitivity analysis revealed that stiffness-related parameters exert the strongest and most consistent influence on knee kinematics, motivating their prioritization during tuning. The framework was experimentally validated through treadmill walking trials involving able-bodied individuals and an amputee participant by adjusting stiffness-related PC weights to achieve multiple predefined target knee profiles. Results demonstrate that diverse target behaviors can be reliably achieved using structured low-dimensional tuning. This study establishes PCA-based parameterization as an effective strategy for simplifying continuous impedance tuning and provides a principled foundation for personalized prosthetic control, in which target behaviors may be defined through user feedback, clinician input, or adaptive learning.