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Real-time visual and EMG signals recognition to control dexterous prosthetic hand based on deep learning and machine learning

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Experiment demonstrates effective classification of gripping patterns in prosthetic hands using EMG and image recognition.

Proposed method achieves 95% recognition accuracy in controlling prosthetic hands using EMG signals.
EMG signals collected from five healthy participants reached an accuracy of 89% using SVM analysis.
GoogLeNet algorithm successfully sorted 723 RGB images into seven gripping classes with 99% training accuracy.
The innovative Vision-EMG approach combines image recognition with myoelectric control for better prosthetic function.

Mahmood et al. (2021) studied this question.

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