'Holy Grail' of Bionic Tech Teaches Itself How Amputees Move

This self-learning bionic arm gives a new immersive plateau of intuitive, natural use to amputees.
Brad Bergan

The "holy grail" in neuroprosthetics is to provide intuitive, natural, and real-time movement with bionic limbs, and a new study shows how a team of intrepid scientists really pulled it off.


The 'holy grail' of bionic tech

Earlier advances in the field have led to mind-controlled systems — even ones with kinaesthetic (or haptic) feedback — but existing systems have so far required a lot of effort, learning, and practice from the amputee, reports MedicalXpress.

The study, published in the journal Science Translational Medicine, shows how scientists at the University of Michigan designed neuroprosthetic technology capable of restoring a sense of intuitive movement to amputees from the word "go" — without the learning curve.

A major challenge eluding scientists in mind-controlled bionics lies in generating a strong and stable nerve signal for the bionic limb attachment. Scientists have known that peripheral nerves — a system of minuscule nerves fanning out to the brain and spinal column — are capable of the most precise and intuitive control of bionic limbs. However, receiving a nerve signal artificially is difficult because they're both small and significantly muted by scar tissue.

The team of scientists at the University of Michigan solved this problem by wrapping tiny muscle grafts around participants' arms. The muscle grafts functioned as regenerative "interfaces," which stalled the growth of scar tissue, and also provided a novel way to amplify nerve signals.

"To my knowledge, we've seen the largest voltage recorded from a nerve compared to all previous results," said Cindy Chestek, associate professor of biomedical engineering at the University of Michigan. "In previous approaches, you might get 5 microvolts or 50 microvolts — very very small signals. We've seen the first ever millivolt signals."

Boosting signals with algorithms

The university team also developed algorithms to help the bionic element "learn" how to adapt to amputees' movements.

"You can make a prosthetic hand do a lot of things, but that doesn't mean that the person is intuitively controlling it. The difference is when it works on the first try just by thinking about it, and that's what our approach offers," said Chestek. "This worked the very first time we tried it. There's no learning for the participants. All of the learning happens in our algorithms. That's different from other approaches."

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Participants in the tests were able to lift blocks with a pincer grip, rotate their thumb in a continuous motion, pick up spherical objects, and even play Rock, Paper, Scissors. "It's like you have a hand again," said Joe Hamilton, a participant who lost an arm in a fireworks incident. "You can do pretty much anything you can do with a real hand with that hand. It brings you back to a sense of normalcy."

As the most incredible advancement in neuroprosthetics for many years, this technology represents a paradigm shift in the way bionics work. Of course, there's always more to be done: "It's going to be a ways from here, but we're not going to stop working on this until we can completely restore able-bodied hand movements," said Chestek. "That's the dream of neuroprosthetics."