Researchers from the University of Michigan (MI, USA) have developed a novel neuroprosthetic model involving muscle grafts with machine-learning algorithms, which allowed for ultra-precise movements in a prosthetic hand. This technology is considered to be a major advance in motor control for amputees and could significantly enhance quality of life for individuals with upper limb loss.
Peripheral nerve interfaces control neuroprosthetics by registering nerve signals in the remaining limb and translating them into movements. This allows individuals who have lost limbs to intuitively control prosthetic replacements. However, one of the biggest challenges in mind-controlled prosthetics is establishing a strong and stable nerve signal to translate to the bionic limb that will allow for a range of precise and durable movements.
Previous studies have investigated nerve signals in the brain as a potential source. However, this is invasive and high risk.
An alternative approach is to explore the peripheral nerves. The nerve signals they carry are small, so in order to overcome this issue, researchers from the University of Michigan developed the regenerative peripheral nerve interface (RPNI). This consists of a peripheral nerve implanted into a graft of muscle combined with machine-learning algorithms borrowed from the brain–machine interface field. This technology prevents the growth of neuromas and amplifies nerve signals.
In this study, recently published in Science Translational Medicine, the team implanted these RPNIs into the arms of four upper limb amputees and tested their durability and function.
When implanting electrodes into the muscle grafts of two individuals, the researchers discovered that the electrodes were able to record their nerve signals and pass them onto a prosthetic hand in real time.
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“To my knowledge, we’ve seen the largest voltage recorded from a nerve compared to all previous results,” commented co-lead author of the study, Cindy Chestek (University of Michigan College of Engineering). “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 team observed that the neuroprosthetic control worked on the first try and didn’t require any learning from the participant.
“It’s like you have a hand again,” remarked study participant Joe Hamilton. “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.”
Further, the interface worked for up to 300 days without requiring recalibration.
“This is the biggest advance in motor control for people with amputations in many years,” commented Paul Cederna (University of Michigan Medical School), who also co-led the study. “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.”
The authors advise that further studies are necessary to compare the benefits of RPNI with other approaches.
Chestek concluded: “We’re not going to stop working on this until we can completely restore able-bodied hand movements. That’s the dream of neuroprosthetics.”
Sources: Vu PP, Vaskov ASK, Irwin ZT et al. A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees. Sci. Transl. Med. (2020); www.eurekalert.org/emb_releases/2020-03/uom-ly022720.php; www.eurekalert.org/emb_releases/2020-03/aaft-rni030220.php