Ultra-precise, mind-controlled prosthetic hand for amputees via RPNI neural interface

“It’s like you have a hand again”: A new study from the University of Michigan gives amputees natural, finger-level control of a robotic hand:

In this major advance for mind-controlled prosthetics, U-M research led by Paul Cederna and Cindy Chestek demonstrates an ultra-precise prosthetic interface technology that taps faint latent signals from nerves in the arm and amplifies them to enable real-time, intuitive, finger-level control of a robotic hand.

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U-M’s approach to neuroprosthetics centers on the Regenerative Peripheral Nerve Interface (RPNI)—a small graft of muscle tissue surgically attached to the end of a severed nerve in an amputee’s arm.

While other neural interfaces are harmful to nerves, the RPNI promotes healthy nerve growth and acts as a bioamplifier, converting faint neural signals sent from the brain into large, recordable muscle signals that remain stable for years. Combined with machine learning algorithms, these signals enable intuitive, real-time mind control of advanced robotic prosthetic hands.

The research is published in the journal Science Translational Medicine and is titled, “A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees.”

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Paul Cederna is the Robert Oneal Collegiate Professor of Plastic Surgery and a professor of biomedical engineering.

Cindy Chestek is an associate professor of biomedical engineering and part of U-M’s Robotics Institute.

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