A Clinical Tuning Framework for Continuous Kinematic and Impedance Control of a Powered Knee-Ankle Prosthesis
🤖 AI Summary
To address the low clinical efficiency of personalized configuration and poor cross-task generalization in multimodal powered knee-ankle prostheses, this study proposes a continuous kinematic/impedance hybrid controller with a clinical-grade real-time parameter tuning interface (CTI). Methodologically, we introduce a novel parallel kinematic/dynamic individualized modeling framework that generalizes from level-ground gait data alone to incline/decline walking and sit-to-stand transitions. The architecture integrates phase- and task-driven control, kinematic individuality prediction, parallel estimation of kinetic individuality, graphical parameter mapping, and real-time model regeneration. Experimental results demonstrate that clinicians can fully tune the controller for all tasks—level walking, ramp ascent/descent, and sit-to-stand—in under 20 minutes, with each iteration requiring only 2 minutes for walking and 1 minute for sit-to-stand. The prosthesis accurately tracks both manually specified and automatically generalized torque commands, achieving over an order-of-magnitude improvement in tuning efficiency.
Application Category
📝 Abstract
Configuring a prosthetic leg is an integral part of the fitting process, but the personalization of a multi-modal powered knee-ankle prosthesis is often too complex to realize in a clinical environment. This paper develops both the technical means to individualize a hybrid kinematic-impedance controller for variable-incline walking and sit-stand transitions, and an intuitive Clinical Tuning Interface (CTI) that allows prosthetists to directly modify the controller behavior. Utilizing an established method for predicting kinematic gait individuality alongside a new parallel approach for kinetic individuality, we personalize continuous-phase/task models of joint impedance (during stance) and kinematics (during swing) using tuned characteristics exclusively from level-ground walking. To take advantage of this method, we developed a CTI that translates common clinical tuning parameters into model adjustments for the walking and sit-stand controllers. We then conducted a case study where a prosthetist iteratively tuned the powered prosthesis to an above-knee amputee participant in a simulated clinical session involving sit-stand transitions and level walking, from which incline/decline walking features were automatically calibrated. The prosthetist fully tuned the multi-activity prosthesis controller in under 20 min. Each iteration of tuning (i.e., observation, parameter adjustment, and model reprocessing) took 2 min on average for walking and 1 min on average for sit-stand. The tuned behavior changes were appropriately manifested in the commanded prosthesis torques, both at the manually tuned tasks and automatically tuned tasks (inclines). This paper introduces a clinical tuning interface that simplifies the tuning process for multimodal robotic prosthetic legs, reducing the time required from several hours to just 20 min thus improving clinical feasibility.
Problem
Research questions and friction points this paper is trying to address.
Personalizing multi-modal powered knee-ankle prosthesis control in clinics
Simplifying tuning for hybrid kinematic-impedance prosthetic controllers
Reducing clinical tuning time from hours to 20 minutes
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid kinematic-impedance controller for variable-incline walking
Clinical Tuning Interface simplifies prosthesis personalization
Parallel approach for kinetic and kinematic gait individuality
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