ArticlePloS one2018
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Dimensionality Reduction for Offline Alphabet Arabic Sign Language Recognition using Deep Learning.Journal of biomedical physics & engineering · 2026Article
- A Co-Located sEMG-pFMG Dataset for Hand Gesture Recognition Under Varying Arm-Position Conditions.Sensors (Basel, Switzerland) · 2026Article
- Spatiotemporal Dual-Channel Interpretable Hybrid Neural Network for HD-sEMG-Based Gesture Recognition.Sensors (Basel, Switzerland) · 2026Article
- Myoelectric Gesture Recognition Based on Multiple Mapping and Deep Neural Network.Biomimetics (Basel, Switzerland) · 2026Article
- Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames.Sensors (Basel, Switzerland) · 2026Article
- Few-shot prototype adaptation for generalizable electromyography gesture recognition.Scientific reports · 2026Article
- A multi-scale dual-stream fusion network for high-accuracy sEMG-based gesture classification.Scientific reports · 2026Article
- Hybrid Convolutional Vision Transformer for Robust Low-Channel sEMG Hand Gesture Recognition: A Comparative Study with CNNs.Biomimetics (Basel, Switzerland) · 2025Article
- Upper limb human-exoskeleton system motion state classification based on semg: application of CNN-BiLSTM-attention model.Scientific reports · 2025Article
- Automatic gait EVENT detection in older adults during perturbed walking.Journal of neuroengineering and rehabilitation · 2025Article
- Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation.Sensors (Basel, Switzerland) · 2025Article
- Medium density EMG armband for gesture recognition.Frontiers in neurorobotics · 2025Article
- Research on sports activity behavior prediction based on electromyography signal collection and intelligent sensing channel.PeerJ. Computer science · 2025Article
- sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules.Frontiers in bioengineering and biotechnology · 2025Article
- A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition.Scientific reports · 2024Article
- Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture.Scientific reports · 2024Article
- [Gesture accuracy recognition based on grayscale image of surface electromyogram signal and multi-view convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- MS-CLSTM: Myoelectric Manipulator Gesture Recognition Based on Multi-Scale Feature Fusion CNN-LSTM Network.Biomimetics (Basel, Switzerland) · 2024Article
- A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices.Sensors (Basel, Switzerland) · 2024Article
- Hand gesture recognition using sEMG signals with a multi-stream time-varying feature enhancement approach.Scientific reports · 2024Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) architecture which captures spatial information of electromyogram signal. Motivated by the sequential nature of electromyogram signal, we propose an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture recognition problem. Moreover, we present a new sEMG image representation method based on a traditional feature vector which enables deep learning architectures to extract implicit correlations between different channels for sparse multi-channel electromyogram signal. Extensive experiments on five sEMG benchmark databases show that the proposed method outperforms all reported state-of-the-art methods on both sparse multi-channel and high-density sEMG databases. To compare with the existing works, we set the window length to 200ms for NinaProDB1 and NinaProDB2, and 150ms for BioPatRec sub-database, CapgMyo sub-database, and csl-hdemg databases. The recognition accuracies of the aforementioned benchmark databases are 87.0%, 82.2%, 94.1%, 99.7% and 94.5%, which are 9.2%, 3.5%, 1.2%, 0.2% and 5.2% higher than the state-of-the-art performance, respectively.
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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.