Machine Learning and VR Are Driving Prosthetics Research

Fitting a patient for a prosthetic limb is normally a painstaking and time-consuming process. In some cases, trying to determine how capable a patient may be of operating a prosthetic limb even before fitting one has also been a problem. However, using virtual reality and reinforcement learning, researchers in North Carolina and Arizona are revealing new technologies and techniques to make prosthetic fitting more convenient for both patients and clinicians: In Charlotte, surgeons at OrthoCarolina used VR to demonstrate that patients born without hands had inborn abilities to control prosthetic hands without prerequisite targeted muscle re-innervation surgery (as often required by traumatic amputee patients). In Raleigh, Chapel Hill, and Tempe,AZ., engineering professors demonstrated a tuning algorithm based on reinforcement learning could reduce the time needed to fit a robotic knee from hours to about 10 minutes. The researchers say the breakthroughs indicate a new era of convenience and optimism may be in the offing for amputees.