AI in Prosthetics and Bionic Limbs

Most bionic arms are myoelectric. Electrodes on the skin inside the socket pick up the small electrical signals, called electromyography or EMG, produced when the user contracts muscles in the residual limb. For decades control was direct: one muscle opened the hand, another closed it, and the user switched between hand and wrist functions with a co-contraction or a button. This works but is slow and tiring. Pattern recognition changed that. Instead of reading one muscle at a time, a classifier reads several electrodes together and learns which overall pattern corresponds to close hand, rotate wrist or rest. Coapt brought this to commercial arms in the early 2010s. Surgery helps too. Targeted muscle reinnervation, developed by Todd Kuiken and colleagues in Chicago, reroutes nerves that once served the missing arm into remaining muscles, creating new signal sites. Multi-grip hands such as the Psyonic Ability Hand and the DEKA LUKE arm offer many grip patterns. For legs, the intelligence sits mostly in the controller. Microprocessor knees such as Ottobock's C-Leg, introduced in the late 1990s, measure knee angle and load many times per second and adjust resistance so the knee stays stable on slopes, stairs and stumbles. Powered knees and ankles add motors. In 2024 an MIT team led by Hugh Herr reported in Nature Medicine that people who had a surgical technique called the agonist-antagonist myoneural interface walked faster and more naturally with a bionic ankle under their own neural control. The limits are real. EMG changes with sweat, fatigue, electrode shift and arm position, so lab accuracy often drops at home. Most devices give little or no sense of touch, are heavy and expensive, and many users abandon upper-limb prostheses. A common misconception is that bionic limbs read thoughts directly. Most read muscles, not the brain.