Caroline Brogan

02 July 2018 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.

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.” Current technology works by directly controlling the prosthetic motors with a few muscular signals. The new bionic hand, developed in collaboration with Imperial and the University of Göttingen, uses a human-machine interface that interprets the patient’s intentions and sends commands to the artificial limb.

It contains eight electrodes that pick up weak electrical signals from the patient’s stump, before amplifying them and sending them to a mini-computer, also located in the prosthetic. The mini-computer then runs the machine learning algorithm to interpret the signals, before commanding the hand’s motors to move in the way the patient wants.

Patients found they were able to easily rotate the wrist and open the hand either simultaneously or separately. They also found the movements far more natural than the conventional bionic limbs they were used to. Read more from…

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