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Found 5 papers, 4 papers with code
A Flexible and Modular Body-Machine Interface for Individuals Living with Severe Disabilities
The measured prediction performances show an average accuracy of 99. 96% for able-bodied individuals and 91. 66% for participants with upper-body disabilities.
Human-Computer Interaction Robotics
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.
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
