Mind over hand gives amputees motion

Faint nerves give amputees control of robotic hand
Joe Hamilton, a participant in the University of Michigan RPNI study, naturally uses his mind to control a DEKA prosthetic hand to pinch a small zipper on a hand development testing platform. (Evan Dougherty, Michigan Engineering)

University of Michigan researchers have tapped faint, latent signals from arm nerves and amplified them to enable real-time, intuitive, finger-level control of a robotic hand.

This development may be a big step in giving amputees the ability to achieve prosthetic control. The researchers developed a way to tame temperamental nerve endings, separate thick nerve bundles into smaller fibers that enable more precise control, and amplify the signals coming through those nerves. The approach involves tiny muscle grafts and machine learning algorithms borrowed from the brain-machine interface field.

“This is the biggest advance in motor control for people with amputations in many years,” said Paul Cederna, Robert Oneal Collegiate Professor of Plastic Surgery at the U-M Medical School, as well as a professor of biomedical engineering. Cederna co-leads the research with Cindy Chestek, associate professor of biomedical engineering at the U-M College of Engineering.

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The paper developed by the researchers is titled, “A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees.” It describes results with four study participants using the Mobius Bionics LUKE arm.

“You can make a prosthetic hand do a lot of thngs, 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,” Chestek said. “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.”

In the lab, study partcipants were able to pick up blocks with a pincer grasp; move their thumb in a continuous motion, rather than have to choose from two positions; lift spherically shaped objects; and even play in a version of Rock, Paper, Scissors called Rock, Paper, Pliers.

“It’s like you have a hand again,” said study participant Joe Hamilton, who lost his arm in a fireworks accident in 2013. “You can pretty much do anything you can do with a real hand with that hand. It brings you back to a sense of normalcy.”

One of the biggest hurdles in mind-controlled prosthetics is tapping into a strong and stable nerve signal to feed the bionic limb. Some research groups—those working in the brain-machine interface field—go all the way to the primary source, the brain. While this is necessary when working with people who are paralyzed, the approach is also invasive and high-risk.

Instead, the University of Michigan researchers wrapped tiny muscle grafts around the nerve endings in the participants’ arms. These “regenerative peripheral nerve interfaces,” or RPNIs, offer severed nerves new tissue to latch on to. This prevents the growth of nerve masses called neuromas that lead to phantom limb pain. And it gives the nerves a megaphone. The muscle grafts amplify the nerve signals. Two patients had electrodes implanted in their muscle grafts, and the electrodes were able to record these nerve signals and pass them on to a prosthetic hand in real time.

“To my knowledge, we’ve seen the largest voltage recorded from a nerve compared to all previous results,” Chestek said. “In previous approaches, you might get 5 microvolts or 50 microvolts—very very small signals. We’ve seen the first ever millivolt signals.

“So now we can access the signals associated with individual thumb movement, multidegree of freedom thumb movement, individual fingers. This opens up a whole new world for people who are upper limb prosthesis users.”

The findings also open up new possibilities for the field, said Chestek, whose expertise is on real-time machine learning algorithms to translate neural signals into movement intent.

“What we found is now the nerve signals are good enough to apply the whole world of things we learned in brain control algorithms to nerve control,” she said.

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