Mind-controlled prosthetics (artificial limbs) may soon work dramatically better, thanks to AI algos.

“A Regenerative Peripheral Nerve Interface (RPNI) is a small graft of muscle tissue surgically attached to the end of a severed nerve in an amputee’s arm. The RPNI promotes healthy nerve growth and acts as a bio amplifier. It converts 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.” – (Mind Control Prosthesis, University of Michigan Research paper)

The brain continues to send electronic signals through the nervous system to the ends of nerves severed by the amputation of a limb. However, these signals, which continue for years, were too faint to be used for the control of a prosthetic, or bionic limb.

Using an RPNI as a bio amplifier

Researchers at the University of Michigan created an RPNI by cutting an amputee’s peripheral nerve and stitching it to a muscle. The subsequent healing in the area helps develop new nerves and blood vessels over three months. Nerve signals landing here from the brain are amplified and become strong. Therefore, they are strong enough (in millivolts) to transmit to the artificial limb. (MIT Technology Review)

Researchers implanted electrodes into one or more of such RPNIs. The electrodes pick up these signals and pass them onto the artificial limb in real-time.

AI and Machine Learning at the last mile

Machine learning algorithms convert these signals into movements executed by the prosthetic. According to study co-leader Cynthia Chestek, associate professor, the implants worked fine over 300 days of testing without recalibration.

Four amputees successfully participated in the study. The procedure allowed them fine control of the artificial limbs. They could pick up miniature play bricks, grasp items like soda cans, and play Rock, Paper, Scissors.

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