ReviewFrontiers in neuroscience2021
Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection.Journal of neuroengineering and rehabilitation · 2024Pooled it
- Specifications and functional impact of a self-triggered grasp neuroprosthesis developed to restore prehension in hemiparetic post-stroke subjects.Biomedical engineering online · 2024Trial
- Electrode-Level Low-Dimensionality Does Not Guarantee Sensor Redundancy: Dual-Dataset, Participant-Grouped Validation of Parsimonious Myoelectric Gesture Decoding.Biomimetics (Basel, Switzerland) · 2026Article
- Sensor Topology-Aware Three-Branch Fusion for sEMG Gesture Recognition.Sensors (Basel, Switzerland) · 2026Article
- How few channels suffice? A hierarchical validation framework for minimal-channel wearable sEMG in upper-limb rehabilitation gesture recognition.Journal of neuroengineering and rehabilitation · 2026Article
- Multigesture Electromyographic Control Complexity in Upper Limb Prostheses Actuated via Single Sensor Input Contraction Magnitude: Qualitative Study for Evaluating Performance and Cognitive Load.JMIR rehabilitation and assistive technologies · 2026Article
- Surface EMG-Based Hand Gesture Recognition Using a Hybrid Multistream Deep Learning Architecture.Sensors (Basel, Switzerland) · 2026Article
- Few-shot prototype adaptation for generalizable electromyography gesture recognition.Scientific reports · 2026Article
- Review
- A multi-scale dual-stream fusion network for high-accuracy sEMG-based gesture classification.Scientific reports · 2026Article
- Cloud-Based Personalized sEMG Classification Using Lightweight CNNs for Long-Term Haptic Communication in Deaf-Blind Individuals.Bioengineering (Basel, Switzerland) · 2025Article
- Automated assessment of upper limb spasticity in stroke patients with fusion of multichannel surface electromyography features.Journal of rehabilitation medicine · 2025Article
- A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures.Scientific data · 2025Article
- Surface electromyography markers of motor learning during laparoscopic skill acquisition: associations with performance and movement efficiency.Journal of robotic surgery · 2025Article
- A Highly Efficient HMI Algorithm for Controlling a Multi-Degree-of-Freedom Prosthetic Hand Using Sonomyography.Sensors (Basel, Switzerland) · 2025Article
- Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition.Scientific data · 2025Article
- Multimodal Deep Learning Model for Cylindrical Grasp Prediction Using Surface Electromyography and Contextual Data During Reaching.Biomimetics (Basel, Switzerland) · 2025Article
- [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
- Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.
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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.