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Technology has made incredible advancements in the development of artificial hands for amputees. These advanced prostheses allow for independent finger movements and wrist rotation, controlled by a smartphone app or muscle signals from the forearm. The patient can learn to control different finger movements through rehabilitation, and now, researchers are using artificial intelligence to make controlling advanced hand prostheses more intuitive. With the help of the “synergy principle” and 128 sensors on the forearm, patients can have more natural control over their prosthetic hands.
The “synergy principle” is based on the idea that the brain activates a pool of muscle cells in the forearm to adapt to the complex movements involved in grasping objects. By applying this principle to artificial hand design and control, new learning algorithms are being created to make movements more fluid and seamless. Machine learning is helping to improve control adaptability and the learning process, making it easier for patients to use their prosthetic hands in their daily lives.
Experiments with this new approach have shown promising results, suggesting that more advanced strategies could soon empower conventional control methods for artificial hands. By studying muscle activation with up to 64 sensors on the inside and outside of the forearm, researchers are gaining a better understanding of the electrical signals transmitted by the spinal motor neurons.