Controlling Locomotion Over Continuously Varying Activities for Agile Powered Prosthetic Legs

Amputation Clinical Trial

Official title: Controlling Locomotion Over Continuously Varying Activities for Agile Powered Prosthetic Legs

Status Recruiting

Clinical Trial Summary

The overall goal of this project is to model human joint biomechanics over continuously-varying locomotion to enable adaptive control of powered above-knee prostheses. The central hypothesis of this project is that variable joint impedance can be parameterized by a continuous model based on measurable quantities called phase and task variables. This project will use machine learning to identify variable impedance functions from able-bodied data including joint perturbation responses across the phase/task space to bias the solution toward biological values.

Clinical Trial Description

The overall goal of this project is to model human joint biomechanics over continuously-varying locomotion to enable adaptive control of powered above-knee prostheses. Above-knee amputees often struggle to perform the varying activities of daily life with conventional prostheses due to the lack of positive mechanical work and active control. Emerging powered prostheses have motors that can perform these missing functions, but the biomechanics experienced by the user depend on the control of these motors. The way the prosthesis interacts with both the user and environment can be controlled through joint impedance--the relationship between joint motion and torque. Prosthetic joint impedance is typically defined via a stiffness, viscosity, and equilibrium angle for discrete phases of gait within a limited set of discrete activities, but this framework does not allow continuous variations of steady-state activities (e.g., walking at different speeds/inclines) or continuous transitions between activities (e.g., walk to stair ascent). The central hypothesis of this project is that variable joint impedance can be parameterized by a continuous model based on measurable quantities called phase and task variables. This project will use machine learning to identify variable impedance functions from able-bodied data including joint perturbation responses across the phase/task space to bias the solution toward biological values. The resulting impedance model will be used with real-time estimates of phase and task variables to control a custom powered knee-ankle prosthesis and the Ossur PowerKnee across activities. The clinical trial will comprise the following human subject experiments. Aim 1.3: N=5 able-bodied subjects will be recruited for initial testing of the walking and stair controllers. Once the powered knee-ankle prosthesis achieves satisfactory performance, we will enroll N=5 amputee subjects to validate these controllers. Aim 2.3: N=5 able-bodied subjects will be recruited for initial testing of the sit-to-stand and walk-stair transition controllers. Once the powered knee-ankle prosthesis achieves satisfactory performance, we will enroll N=5 amputee subjects to validate these controllers. Aim 3.1: N=5 amputee subjects will be enrolled to validate the clinical interface for the powered prosthesis controllers. Aim 3.2: N=5 amputee subjects will be enrolled to validate the transfer of the controllers to the PowerKnee. Aim 3.3: N=10 amputee subjects will be enrolled in a study of endurance and symmetry outcomes with the PowerKnee compared to their take-home prosthesis. ;

Related Conditions & MeSH terms

• Amputation

• NCT number: NCT06138977
• Study type: Interventional
• Source: University of Michigan
• Contact: Emily Klinkman, MS
• Phone: 734-846-0046
• Email: emilykk@umich.edu
• Status: Recruiting
• Phase: N/A
• Start date: December 6, 2023
• Completion date: January 30, 2028