Self-learning bionic hand could spark ’new generation’ of prosthetic limbs

The new prosthetic hand interprets muscular signals from brain activity with machine learning to make movements more natural. Scientists at Imperial College London and the University of Göttingen have used machine learning to improve the performance of prosthetic hands. The new bionic hand is not only more natural but it is also superior in terms of functionality in daily tasks than what's currently available to patients. Professor Dario Farina Department of Bioengineering After testing their prototype on five amputees , they found that new machine learning-based control was far better at providing natural, fluid movements than the currently available technology. The researchers said the findings , which are published in Science Robotics , could spark a "new generation of prosthetic limbs." Professor Dario Farina , senior author of the paper from Imperial's Department of Bioengineering , said: "When designing bionic limbs, our main goal is to let patients control them as naturally as though they were their biological limbs. This new technology takes us a step closer to achieving this." - Machine learning. Current technology works by directly controlling the prosthetic motors with a few muscular signals.