Improving Fast EMG Classification for Hand Gesture Recognition: A Comprehensive Analysis of Temporal, Spatial, and Algorithm Configurations for Healthy and Post-Stroke Subjects

Electromyography-based assistive and rehabilitation devices have shown potential for restoring mobility, especially for post-stroke patients. However, the variability of biological signals and the processing delays caused by signal acquisition and feature extraction influence myoelectric control sys...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 22; S. 6980
Hauptverfasser: Montecinos, Camila, Espinoza, Jessica, Zamora Zapata, Mónica, Meruane, Viviana, Fernandez, Ruben
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 15.11.2025
Schlagworte:
Accuracy
Adult
Aged
Algorithms
Analysis
assistive technology
Classification
classification algorithms
Data mining
Datasets
electromyography
Electromyography - methods
feature extraction
Female
Gestures
Hand - physiology
Hand - physiopathology
Humans
Machine Learning
Male
Middle Aged
Muscle function
myoelectric control
Rehabilitation
Signal processing
Signal Processing, Computer-Assisted
Stroke
Stroke - physiopathology
Stroke Rehabilitation
Support vector machines
Wavelet transforms
classification algorithms
dimensionality reduction
feature extraction
electromyography
myoelectric control
assistive technology
machine learning
stroke
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Electromyography-based assistive and rehabilitation devices have shown potential for restoring mobility, especially for post-stroke patients. However, the variability of biological signals and the processing delays caused by signal acquisition and feature extraction influence myoelectric control systems’ real-time functionality and robustness. This study evaluates the classification performance of electromyographic (EMG) signals for six distinct hand gestures in healthy individuals and post-stroke patients. Different feature extraction methods and machine learning algorithms are employed to analyze the impact of acquisition time (0.5–4 s) and the number of channels (1–4) on model accuracy, robustness, and generalization. The best results are obtained using power spectral density and dimensionality reduction, reaching a classification accuracy of 94.79% with a 2 s signal and 95.31% for 4 s. Acquisition time has a greater effect on accuracy than the number of channels used with accuracy stabilizing at 2 s. We test for generalization using post-stroke patient data, evaluating two scenarios: intra-patient validation with 90% accuracy and cross-patient validation with 35–40% accuracy. This study contributes to developing effective real-time myoelectric control systems for neurorehabilitation.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25226980