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Scientists develop new hand gesture recognition model with 96.92% accuracy, aiding prosthetic control.
Scientists at Shanghai Jiao Tong University have created a new model for recognizing hand gestures that uses advanced neural network technology to process muscle signals into two-dimensional images.
This method, called cwCST-CNN, significantly improves classification accuracy to 96.92%, surpassing existing methods.
It has promising applications in prosthetic control and rehabilitation training.
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Los científicos desarrollan un nuevo modelo de reconocimiento de gestos de mano con una precisión del 96,92%, ayudando al control protésico.