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. "It's a very small population," OrthoCarolina hand surgeon Michael Gart, MD, said of the work he did with colleagues Bryan Loeffler, MD, and Glenn Gaston, MD Gart estimated the congenital amputee clinic at OrthoCarolina serves a population catchment area of about one million people and had only seven to nine patients who fit the criteria for the VR study. "Thankfully, most or all were willing and excited to participate in the study," Gart said. "It...
Source: MDDI - Category: Medical Devices Authors: Tags: Digital Health Source Type: news