Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based Feature Selection Methods

Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorit...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 15; s. 6939
Hlavní autoři: Zhang, Yue, Sun, Maoxun, Xia, Chunming, Zhou, Jie, Cao, Gangsheng, Wu, Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 04.08.2023
MDPI
Témata:
Accuracy
Algorithms
Ankle
chameleon swarm algorithm
Classification
Computational linguistics
Deep learning
Experiments
Feature selection
Gait
grasshopper optimization algorithm
Language processing
Machine learning
Mathematical optimization
mechanomyography
Methods
Natural language interfaces
Optimization algorithms
Pattern recognition
Rehabilitation
Signal processing
Swarm intelligence
Wavelet transforms
China
grasshopper optimization algorithm
mechanomyography
chameleon swarm algorithm
feature selection
pattern recognition
ISSN:1424-8220, 1424-8220
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Shrnutí:Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are proposed for the pattern recognition of knee and ankle movements in the sitting and standing positions. Wireless multichannel MMG acquisition systems were designed and used to collect MMG movements from four sites on the subjects thighs. The relationship between the threshold values and classification accuracy was analyzed, and comparatively high recognition rates were obtained after redundant information was eliminated. When the threshold value rose, the recognition rates from the CSA fluctuated within a small range: up to 88.17% (sitting position) and 90.07% (standing position). However, the recognition rates from the GOA drop dramatically when increasing the threshold value. The comparison results demonstrated that using a GOA consumes less time and selects fewer features, while a CSA gives higher recognition rates of knee and ankle movements.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23156939