STGCN-GR applies graph convolutions to HD-sEMG data and reports 91.07% accuracy on 65 gestures, exceeding prior deep learning baselines on the same public dataset.
Graph neural networks for HD EMG-based movement intention recognition: an initial investigation
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A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography
STGCN-GR applies graph convolutions to HD-sEMG data and reports 91.07% accuracy on 65 gestures, exceeding prior deep learning baselines on the same public dataset.