Controlling Locomotion Over Continuously Varying Activities for Agile Powered Prosthetic Legs

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.