ArticleFrontiers in bioengineering and biotechnology2021
Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed.
- Systematic Evaluation of sEMG Processing Pipelines for Gesture Recognition.Bioengineering (Basel, Switzerland) · 2026Article
- Dual-Stream SPP-CNN for High-Precision sEMG Gesture Recognition in Human-Machine Interfaces.Biomimetics (Basel, Switzerland) · 2026Article
- EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.Scientific reports · 2026Article
- Forearm Radiomyography for Character and Writer Identification in Air Writing.IEEE journal of electromagnetics, RF and microwaves in medicine and biology · 2026Article
- A Lightweight Frozen Multi-Convolution Dual-Branch Network for Efficient sEMG-Based Gesture Recognition.Sensors (Basel, Switzerland) · 2026Article
- Ultrasound and Unsupervised Segmentation-Based Gesture Recognition for Smart Device Unlocking.Sensors (Basel, Switzerland) · 2025Article
- Local pattern aware 3D video swin transformer with masked autoencoding for realtime augmented reality gesture interaction.Scientific reports · 2025Article
- sEMG-Based Gesture Recognition Using Sigimg-GADF-MTF and Multi-Stream Convolutional Neural Network.Sensors (Basel, Switzerland) · 2025Article
- sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules.Frontiers in bioengineering and biotechnology · 2025Article
- An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention.Scientific reports · 2024Article
- Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network.Heliyon · 2024Article
- Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion.BMC medical informatics and decision making · 2022Article
- Dynamic gesture recognition based on 2D convolutional neural network and feature fusion.Scientific reports · 2022Article
- Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- National Ballad Creation Education Under Artificial Intelligence and Big Data.Frontiers in psychology · 2022Article
- Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- Surface Defect Segmentation Algorithm of Steel Plate Based on Geometric Median Filter Pruning.Frontiers in bioengineering and biotechnology · 2022Article
- Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network.Frontiers in plant science · 2022Article
- Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network.Frontiers in bioengineering and biotechnology · 2022Article
- Study on exercise muscle fatigue based on sEMG and ECG data fusion and temporal convolutional network.PloS one · 2022Article
Corrections and comments
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Authors and funding
11 authors.
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Abstract
Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.
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