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.

I. Introduction

Lower-limb amputation is a significant disability, pro-foundly affecting individuals both physically and psycholog-ically. The number of major lower-limb amputations in the United States continues to grow, with predictions that the number will double by 2050 compared to 2005 [1]. Globally, more than one million limb amputations are performed annually, which is predicted to increase due to the current global epidemic of diabetes [2]. This loss of mobility and agility in daily life leaves amputees more prone to falls and injuries [3]–[6]. Lower-limb prostheses have evolved and are widely used to replace missing limbs to restore amputees' mobility, stability, and community participation [3], [7].