Existing prosthetic hands have to rely on weak electrical nerve signals in order to know when to activate their motors. That’s because electrodes are typically placed on the skin over the area where the nerves end at the stump and the skin doesn’t transmit electricity that well. Implantable electrodes that make contact with the nerves tend to form scar tissue that ends up ruining signal fidelity and brain-computer interfaces are simply too invasive for most applications.
Now, researchers at the University of Michigan have come up with a way to significantly boost the power of nerve signals so that when they’re captured on the skin they’re strong enough to let users intuitively control them from the first time and with impressive precision.
People who currently use motorized prosthetic arms have to “learn” how to get the devices to do what they want. This can be uncomfortable and a challenge for many, while the quality of movements are not very precise. The new approach allows users to start using their robotic arms immediately after being fitted with one.
All this is possible because the Michigan team was able to manipulate nerve endings, break apart bundles of nerves into smaller fibers, and then implant muscle grafts at the nerve tips to serve as signal amplifiers.
“This is the biggest advance in motor control for people with amputations in many years,” said Paul Cederna, Professor of Plastic Surgery at the University of Michigan Medical School, as well as a professor of biomedical engineering, in a press release. “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.”
Captured signals gathered by the prosthetic are processed using machine learning algorithms that have already proven themselves in brain-machine interfaces. This allows for natively intuitive control of the robotic arms without any learning period.
“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 Cindy Chestek, associate professor of biomedical engineering at the University of Michigan. “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.”
“It’s like you have a hand again,” added study participant Joe Hamilton, who was a victim of a fireworks mishap seven years ago and lost an arm. “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.”
The researchers surrounded nerve endings with very small muscle grafts that they called regenerative peripheral nerve interfaces (RPNIs). These allow nerves to penetrate them and grab on, creating a solid connection and preventing neuromas that are a cause of phantom limb pain. Most importantly, when nerve signals reach the RPNIs, the muscle grafts automatically boost the electric signal significantly.
“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.
The new approach lets users control a prosthetic hand, in this case from Otto Bock, with single finger precision and even multidegree of freedom thumb movement, something that seemed a distant dream.
Here’s a University of Michigan video introducing and showing off what’s possible using the new technology:
Study in Science Translational Medicine: A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees