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Found 3 papers, 2 papers with code
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition
Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines.
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition
The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN.
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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