Towards Personalized Control for Powered Knee Prostheses: Continuous Impedance Functions and PCA-Based Tuning Method

Optimizing control parameters is crucial for personalizing prosthetic devices. The current method of finite state machine impedance control (FSM-IC) allows interaction with the user but requires time-consuming manual tuning. To improve efficiency, we propose a novel approach for tuning knee prostheses using continuous impedance functions (CIFs) and Principal Component Analysis (PCA). The CIFs, which represent stiffness, damping, and equilibrium angle, are modeled as fourth-order polynomials and optimized through convex optimization. By applying PCA to the CIFs, we extract principal components (PCs) that capture common features. The weights of these PCs serve as tuning parameters, allowing us to reconstruct various impedance functions. We validated this approach using data from 10 able-bodied individuals walking. The contributions of this study include: i) generating CIFs via convex optimization; ii) introducing a new tuning space based on the obtained CIFs; and iii) evaluating the feasibility of this tuning space.