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Prosthetic limb users may soon have a bright future to look forward to, in terms of new technology that will be able to help them adapt to the challenge of using the tools.

According to Tech Xplore, the scientists at Imperial College London and the University of Gottingen have successfully utilised machine learning to help improve the performance of prosthetic hands.

The team have tested their prototypes on five different amputees and discovered that the new form of machine learning-based control was actually more adept at providing natural movements than what's currently available to users at this time. The research and the related finding could potentially help to spark a "new generation of prosthetic limbs," according to the researchers.

Professor Dario Farina, who authored the research paper from Imperial College London's Department of Bioengineering, stated that the main goal when designing "bionic" prosthetic limbs is to let patients control them as naturally as possible, as if they were actually their natural limbs, and this new machine-learning technology helps achieve that state as closely as possible.

The latest bionic hand that's being worked on relies on a human-machine interface that actually helps interpret the user's intentions and sends various commands to the artificial limb, as you can see demonstrated in the video below. It’s hooked up to the patient’s stump via electrodes, which amplifies the patient’s signals and sends them to a computer. You can see how naturally and smoothly the hand works on the left, compared to the stilted motions of the limb on the right.

This kind of control allowed patients to more easily rotate their wrist and open their hands simultaneously or separately, and the movements, in general, were more natural than the limbs they had been using previously. These advances are certainly an excellent step forward for ensuring the improvement of lives affected by amputation or missing limbs, and hopefully, the technology will simply continue getting even better from here.

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