ArticleMicromachines2022
A Novel Methodology for Classifying EMG Movements Based on SVM and Genetic Algorithms.
Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG.Sensors (Basel, Switzerland) · 2025Article
- Detecting cognitive motor dissociation by functional near-infrared spectroscopy.Frontiers in neurology · 2025Article
- Surface electromyography dataset from different movements of the hand using a portable and a non-portable device.Data in brief · 2024Article
- Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.Biosensors · 2024Article
- Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals.Sensors (Basel, Switzerland) · 2024Article
- Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.Sensors (Basel, Switzerland) · 2024Article
- Muscle Tone Assessment by Machine Learning Using Surface Electromyography.Sensors (Basel, Switzerland) · 2024Article
- Knee Angle Estimation from Surface EMG during Walking Using Attention-Based Deep Recurrent Neural Networks: Feasibility and Initial Demonstration in Cerebral Palsy.Sensors (Basel, Switzerland) · 2024Article
- Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization.Bioengineering (Basel, Switzerland) · 2024Article
- Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.Bioengineering (Basel, Switzerland) · 2023Article
- Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding.Sensors (Basel, Switzerland) · 2023Article
- Development of a Low-Cost Portable EMG for Measuring the Muscular Activity of Workers in the Field.Sensors (Basel, Switzerland) · 2023Article
- Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.Sensors (Basel, Switzerland) · 2023Article
- Altered Resting-State Brain Activity and Functional Connectivity in Post-Stroke Apathy: An fMRI Study.Brain sciences · 2023Article
- Investigating the Effects of a Focal Muscle Vibration Protocol on Sensorimotor Integration in Healthy Subjects.Brain sciences · 2023Article
- A Deep Learning Approach for Predicting Multiple Sclerosis.Micromachines · 2023Article
- Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps.Brain sciences · 2023Article
- Repeatability and Temporal Consistency of Lower Limb Biomechanical Variables Expressing Interlimb Coordination during the Double-Support Phase in People with and without Stroke Sequelae.Sensors (Basel, Switzerland) · 2023Article
- Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electromyography (EMG) processing is a fundamental part of medical research. It offers the possibility of developing new devices and techniques for the diagnosis, treatment, care, and rehabilitation of patients, in most cases non-invasively. However, EMG signals are random, non-stationary, and non-linear, making their classification difficult. Due to this, it is of vital importance to define which factors are helpful for the classification process. In order to improve this process, it is possible to apply algorithms capable of identifying which features are most important in the categorization process. Algorithms based on metaheuristic methods have demonstrated an ability to search for suitable subsets of features for optimization problems. Therefore, this work proposes a methodology based on genetic algorithms for feature selection to find the parameter space that offers the slightest classification error in 250 ms signal segments. For classification, a support vector machine is used. For this work, two databases were used, the first corresponding to the right upper extremity and the second formed by movements of the right lower extremity. For both databases, a feature space reduction of over 65% was obtained, with a higher average classification efficiency of 91% for the best subset of parameters. In addition, particle swarm optimization (PSO) was applied based on right upper extremity data, obtaining an 88% average error and a 46% reduction for the best subset of parameters. Finally, a sensitivity analysis was applied to the characteristics selected by PSO and genetic algorithms for the database of the right upper extremity, obtaining that the parameters determined by the genetic algorithms show greater sensitivity for the classification process.
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