Electromyography (EMG)
15 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Silent Speech EMG
Subtasks
Latest papers with no code
Electromyography Signal Classification Using Deep Learning
Having implemented this model, an accuracy of 99 percent is achieved on the test data set.
Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage
As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way. In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest.
Simplified markerless stride detection pipeline (sMaSDP) for surface EMG segmentation
In an unconstrained walking experiment, healthy subjects walk through a designed course with their kinematic and EMG data recorded.
Review of medical data analysis based on spiking neural networks
Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions.
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond
In this survey, we assess the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications.
Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies
Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies.
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces
Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms.
Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait
In the GRF analysis, the model showed 94. 78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals.
Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography
One method for improving the classification performance of machine learning models is normalization, such as z-score.
EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement
Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratios, speech noise, or unseen noise types.
