Convolutional sparse autoencoder on two-channel sEMG delivers 94.3% multi-subject F1 for six gestures, 92.3% after few-shot transfer to unseen subjects, and 90% after incremental extension to ten classes.
Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method,
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Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG
Convolutional sparse autoencoder on two-channel sEMG delivers 94.3% multi-subject F1 for six gestures, 92.3% after few-shot transfer to unseen subjects, and 90% after incremental extension to ten classes.