ArticleSensors (Basel, Switzerland)2021
Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 100 citations in OpenAlex.
- EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.Scientific reports · 2026Article
- Hybrid Convolutional Vision Transformer for Robust Low-Channel sEMG Hand Gesture Recognition: A Comparative Study with CNNs.Biomimetics (Basel, Switzerland) · 2025Article
- Sensor-driven control strategies for post-stroke shoulder rehabilitation exoskeletons: A systematic review.MethodsX · 2025Review
- Improving Fast EMG Classification for Hand Gesture Recognition: A Comprehensive Analysis of Temporal, Spatial, and Algorithm Configurations for Healthy and Post-Stroke Subjects.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System.Sensors (Basel, Switzerland) · 2025Article
- Classification of finger movements through optimal EEG channel and feature selection.Frontiers in human neuroscience · 2025Article
- MS-CLSTM: Myoelectric Manipulator Gesture Recognition Based on Multi-Scale Feature Fusion CNN-LSTM Network.Biomimetics (Basel, Switzerland) · 2024Article
- Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.Biosensors · 2024Article
- [Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.Sensors (Basel, Switzerland) · 2024Article
- Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation.Scientific reports · 2024Article
- The Latest Research Progress on Bionic Artificial Hands: A Systematic Review.Micromachines · 2024Review
- Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance.Sensors (Basel, Switzerland) · 2024Article
- Article
- Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals.Scientific reports · 2023Article
- Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
- Efficient strategies for finger movement classification using surface electromyogram signals.Frontiers in neuroscience · 2023Article
- Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks.Micromachines · 2022Article
- Machine Learning for Detection of Muscular Activity from Surface EMG Signals.Sensors (Basel, Switzerland) · 2022Article
- Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks.Behavioural neurology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.
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Registered trials
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.