Researchers have developed new technology for decoding neuromuscular signals to control powered, prosthetic wrists and hands. The work relies on computer models that closely mimic the behavior of the natural structures in the forearm, wrist and hand. The technology could also be used to develop new computer interface devices for applications such as gaming and computer-aided design. Current state-of-the-art prosthetics rely on machine learning to create a "pattern recognition" approach to prosthesis control. This approach requires users to "teach" the device to recognize specific patterns of muscle activity and translate them into commands -- such as opening or closing a prosthetic hand. Instead, the researchers developed a user-generic, musculoskeletal model. The researchers placed electromyography sensors on the forearms of six able-bodied volunteers, tracking exactly which neuromuscular signals were sent when they performed various actions with their wrists and hands. This data was then used to create the generic model, which translated those neuromuscular signals into commands that manipulate a powered prosthetic. In preliminary testing, both able-bodied and amputee volunteers were able to use the model-controlled interface to perform all of the required hand and wrist motions -- despite having very little training.
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