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Found 5 papers, 2 papers with code
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
New subjects only demonstrate the single component gestures and we seek to extrapolate from these to all possible single or combination gestures.
User Training with Error Augmentation for Electromyogram-based Gesture Classification
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration.
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time.
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control
Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach
In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation.
