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.
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges,
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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.