Researchers are looking to employ onboard artificial intelligence systems to improve the control and sophistication of prosthetic hands, by using deep learning approaches that read and react to nerve signals transmitted through the arm. The practice of tracking the natural electric impulses sent by the brain to control individual muscles, known as electromyography, has been used to operate prosthetic limbs and hands before, as well as wheelchairs and other devices. But performance gaps remain when it comes to the fine motor control of fingers and hands. By running a neural network in real-time on a dedicated processing unit within the prosthetic, researchers at the University of Texas at Dallas (UT Dallas) hope to speed up responses for faster hand movements. In addition, the proposed system could be retrained based on the actions of the user to increase its accuracy.

By employing an artificial intelligence network typically used for image recognition, researchers at University of Texas at Dallas aim to skip labor-intensive processing steps while reacting to raw nerve signal data in real time.