ArticleFrontiers in neurorobotics2021
Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning.
Article in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assisting hand gesture classification and rehabilitation assessment via sEMG and finger motion data.Frontiers in bioengineering and biotechnology · 2025Article
- Overcoming behavioral variability in electromyography signals by an adaptive incremental classification approach.Computational and structural biotechnology journal · 2025Article
- Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding.Bioengineering (Basel, Switzerland) · 2023Article
- Adaptive Lower Limb Pattern Recognition for Multi-Day Control.Sensors (Basel, Switzerland) · 2022Article
- Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features.Computational intelligence and neuroscience · 2022Article
- A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of 12 combinations of three feature selection methods [no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)] and four classification methods [non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)] in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (
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