Electromyography (EMG)
15 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Electromyography (EMG)
Silent Speech EMG
Subtasks
Latest papers with no code
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
Neural, Muscular, and Perceptual responses with shoulder exoskeleton use over Days
Over days adaptation to task irrespective of task and group were identified.
Comparison of gait phase detection using traditional machine learning and deep learning techniques
The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model.
High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition
The accuracy of the proposed SNN is 83. 85% and 93. 52% on the two datasets respectively.
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices
This paper presents an algorithm for optimal cut layer selection in terms of maximizing the convergence rate of the model.
Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network
The PINN model is constructed by combining a feed-forward Artificial Neural Network (ANN) with a joint torque computation model.
ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals
Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches.
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN
This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection.
Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients
The inference of the DL model is performed on a low-power microcontroller in the central node.
Electromyography Signal Classification Using Deep Learning
Having implemented this model, an accuracy of 99 percent is achieved on the test data set.
