EMG Gesture Recognition
6 papers with code • 0 benchmarks • 2 datasets
Electromyographic Gesture Recognition
Benchmarks
These leaderboards are used to track progress in EMG Gesture Recognition
NinaPro DB2
Most implemented papers
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Giguelingueling/MyoArmbandDataset
•
•
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.
sEMG Gesture Recognition with a Simple Model of Attention
Myoelectric control is one of the leading areas of research in the field of robotic prosthetics.
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm.
Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks
ketyi/2SRNN
•
•
Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes.
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition
UlysseCoteAllard/LongTermEMG
•
•
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.
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.
