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New Algorithm Personalizes Robotic prosthetics for Better Movement & Health

Researchers have developed a new algorithm that combines two ​processes for personalizing‍ robotic prosthetic devices: optimizing the movement ‌of the prosthetic limb *and* helping the user’s body engage in a more natural⁢ walking pattern. This holistic approach aims to address health challenges associated ​with amputation and improve overall well-being.

The⁣ new approach can be used‌ to help restore and maintain ⁣various aspects of user movement, with the goal of addressing health challenges associated with an amputation.

Beyond Joint ‌Replacement: A Holistic Approach

“Algorithms designed to improve the behavior ⁣ of robotic prosthetics are not new-but this is the first algorithm that also holistically improves the physical behavior⁣ of the‍ person interacting with those prosthetics,” ‌says Varun Nalam, co-lead and co-corresponding author of the research.

“When people have an ⁣amputation above the knee, it affects the way they move othre parts of their body,” explains Nalam, an assistant ⁣research professor in the Lampe⁤ Joint Department ⁤of Biomedical Engineering at north Carolina State⁤ University and the University of North Carolina at Chapel Hill.‌ “that can lead to lower ​back pain, hip problems and ⁤so on. Robotic prostheses to this point have focused on replacing the movement of the missing joint. so, for example, the software that governs the behavior of robotic prosthetic knees ⁢has focused solely on optimizing the ⁤movement of the prosthetic ‍knee joint.

“Our goal with this work was to develop ⁣a new algorithm that allows us to do two things,” says Nalam.⁢ “We still⁣ want to ensure that the prosthetic knee joint is functioning properly-but we also want to ensure that the user’s body is also moving in the same way that it would ⁣have before the amputation. ​This‌ not only gives the user the full range of leg motion, but ​will also​ help to avoid lower back pain, hip problems, et cetera.”

Building on Previous Successes

This research builds upon the team’s prior work in developing an intelligent⁢ system for “tuning” powered prosthetic knees. That ‌system dramatically reduced the ‌tuning time for prosthetic devices, allowing patients to walk comfortably in minutes rather of hours. ‍It was the first system to rely solely on reinforcement learning for prosthetic tuning.

“That work achieved optimal prosthesis control via a reinforcement learning algorithm,” says ‌Helen Huang, senior author of⁤ the paper and a professor of ‌biomedical engineering.

How the Algorithm Works

The new algorithm leverages reinforcement learning ‍to together optimize both prosthetic joint movement⁤ *and* the user’s overall gait.