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Found 2 papers, 1 papers with code
On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living
While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions.
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks.
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