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Real-Time EMG Signal Classification via Recurrent Neural Networks
Real-time classification of Electromyography signals is the most challenging part of controlling a prosthetic hand.
EMG-Based Feature Extraction and Classification for Prosthetic Hand Control
The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to $95. 5\%$ for $800$ msec signal length.
EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available.
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