ArticleSensors (Basel, Switzerland)2022
Machine Learning for Detection of Muscular Activity from Surface EMG Signals.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Detection and Early Detection of Pathological Motor Evoked Potentials: A Novel Approach for Intraoperative Neuromonitoring Assistance.Journal of healthcare informatics research · 2026Article
- An Intelligent Wearable EMG Sensing Framework for Athlete Neuromuscular Monitoring and Performance Progression Assessment.Biosensors · 2026Article
- Real-Time Wearable sEMG Onset Detection and Phase Discrimination of Sit-to-Stand Movement via a Compact Dual-Channel DD-CNN.Sensors (Basel, Switzerland) · 2026Article
- Wearable Wireless EMG Sensors for Monitoring Post-Error Neuromuscular Responses During a Sport-Specific Inhibitory Control Task.Biosensors · 2026Article
- Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Emergency Clinical Decision for Sports Injury Management: A Wearable Sensor-Driven Framework from Training to Rehabilitation.Biosensors · 2026Review
- A Multimodal Biomedical Sensing Approach for Muscle Activation Onset Detection.Sensors (Basel, Switzerland) · 2026Article
- Intra- and Inter-Individual Spectral Pattern Variability of sEMG in Elbow Flexor Motor Tasks.Sensors (Basel, Switzerland) · 2026Article
- Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models.BMC sports science, medicine & rehabilitation · 2025Article
- An adaptive segmentation scheme based on recurring action potentials for sEMG controlled movement decoding.Physical and engineering sciences in medicine · 2025Article
- Optimized strategies for developing high-speed muscle activity monitors utilizing multi-resolution energy operator.Frontiers in bioengineering and biotechnology · 2025Article
- Review of electromyography onset detection methods for real-time control of robotic exoskeletons.Journal of neuroengineering and rehabilitation · 2023Review
- Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMuscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals.
methodsA dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN's performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT).
resultsDEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances.
conclusionsThese outcomes support DEMANN's reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN's adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.