ArticleFrontiers in bioengineering and biotechnology2022
Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition.
Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 61 citations in OpenAlex.
- Systematic Evaluation of sEMG Processing Pipelines for Gesture Recognition.Bioengineering (Basel, Switzerland) · 2026Article
- Optimization of deep learning architecture based on multi-path convolutional neural network algorithm.Scientific reports · 2025Article
- sEMG-Based Gesture Recognition Using Sigimg-GADF-MTF and Multi-Stream Convolutional Neural Network.Sensors (Basel, Switzerland) · 2025Article
- Research on Beef Marbling Grading Algorithm Based on Improved YOLOv8x.Foods (Basel, Switzerland) · 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
- Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals.Sensors (Basel, Switzerland) · 2024Article
- Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework.Briefings in bioinformatics · 2024Article
- A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury.Frontiers in physiology · 2024Article
- Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion.Foods (Basel, Switzerland) · 2023Article
- IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2023Article
- Reinforcement learning based variable damping control of wearable robotic limbs for maintaining astronaut pose during extravehicular activity.Frontiers in neurorobotics · 2023Article
- Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam.Sensors (Basel, Switzerland) · 2022Article
- Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network.Frontiers in bioengineering and biotechnology · 2022Article
- Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.
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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.