Powered prosthetic knee users able to walk in minutes

The system, developed by researchers from North Carolina State University (NC State), the University of North Carolina and Arizona State University (ASU), is said to be the first to rely solely on reinforcement learning to tune the robotic prosthesis.

Robotic prosthetic knees need to be tuned to accommodate the people they are fitted to. According to NC State, the new tuning system tweaks 12 different control parameters, addressing prosthesis dynamics, such as joint stiffness, throughout the entire so-called gait cycle.

A human practitioner would normally work with the patient to modify a handful of parameters, but the process can take hours. The new system relies on a computer program that makes use of reinforcement learning to modify all 12 parameters, which allows patients to use a powered prosthetic knee to walk on a level surface in about 10 minutes.

"We begin by giving a patient a powered prosthetic knee with a randomly selected set of parameters," said Helen Huang, co-author of a paper on the work and a professor in the Joint Department of Biomedical Engineering at NC State and UNC. "We then have the patient begin walking, under controlled circumstances.