Researchers at U-M in Ann Arbor Create More Precise Mind-controlled Prosthetic Hand

Researchers at the University of Michigan in Ann Arbor have tapped faint, latent signals from arm nerves and amplified them to enable real-time, intuitive, finger-level control of a robotic hand that could revolutionize prosthetics.
Joe Hamilton using U-M robotic prosthetic hand
U-M researchers have developed a robotic prosthetic hand that is controlled by amplified nerve signals. Pictured is Joe Hamilton trying it out. // Photo courtesy of the University of Michigan

Researchers at the University of Michigan in Ann Arbor have tapped faint, latent signals from arm nerves and amplified them to enable real-time, intuitive, finger-level control of a robotic hand that could revolutionize prosthetics.

The researchers developed a way to tame nerve endings, separate thick nerve bundles into smaller fibers that enable more precise control, and amplify the signals coming through the nerves. The procedure involves tiny muscle grafts and machine learning algorithms.

“This is the biggest advance in motor control for people with amputations in many years,” says Paul Cederna, the Robert Oneal collegiate professor of plastic surgery at the U-M Medical School, as well as a professor of biomedical engineering. “We have developed a technique to provide individual finger control of prosthetic devices using the nerves in a patient’s residual limb. With it, we have been able to provide some of the most advanced prosthetic control that the world has seen.”

Cederna co-leads the research with Cindy Chestek, associate professor of biomedical engineering. The team studied four participants who used the Mobius Bionics LUKE arm and published their findings in Science Translational Medicine.

“You can make a prosthetic hand do a lot of things, but that doesn’t mean that the person is intuitively controlling it,” Chestek says. “The difference is when it works on the first try just by thinking about it, and that’s what our approach offers. 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.”

Participants can’t yet take the arm home for everyday use, but in the lab, they could pick up blocks with a pincer grasp, move the prosthetic thumb in a continuous motion instead of just two positions, lift spherical objects, and play a version of rock, paper, scissors called rock, paper, pliers.

“It’s like you have a hand again,” says Joe Hamilton, a study participant 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 that work in the brain-machine interface field go all the way to the brain, the primary source. This is necessary when working with people who are paralyzed, but it’s invasive and high-risk.

Peripheral nerves, or the network that fans out from the brain and spinal cord, have not led to a long-term solution for people with amputations because the nerve signals they carry are small. An existing approach to pick up the signals involves probes and leads to scar tissue that muddles an already faint signal over time.

The U-M team wrapped tiny muscle grafts around the nerve endings in patients’ arms so the severed nerves had new tissue to attach to. This prevented the growth of nerve masses called neuromas that lead to phantom limb pain and amplified the nerve signals. Two patients had electrodes implanted in their muscle grafts, and the electrodes were able to record the nerve signals and pass them on to a prosthetic hand.

“To my knowledge, we’ve seen the largest voltage recorded from a nerve compared to all previous results,” Chestek says. “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 process also doesn’t degrade due to the building of scar tissue.

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

“Other research groups have contributed to this as well, but we’ve leapfrogged the capabilities of the prosthetic hands that are currently available,” says Philip Vu, a research fellow in biomedical engineering and first author of the paper. “I think this is strong motivation for further developments from prosthetic hand companies.”

A clinical trial is ongoing, and the team is looking for participants. The research was funded by the Defense Advanced Research Projects Agency and the National Institutes of Health.

The paper is titled “A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees.”

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