Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition

Zhang, Hanyang; Wang, Xinping; Zhang, Yue; Cao, Gangsheng; Xia, Chunming

doi:10.1016/j.bspc.2022.103679

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…Yu et al [ 10 ] collected 4-channel MMG from the thigh and adopted a hidden Markov model to achieve recognition of gait movements. In another study, a Salp Swarm Algorithm (SSA) was used for time-domain feature extraction, and obtained better recognition results compared with some traditional feature-selection algorithms [ 11 ]. Nowadays, deep learning has been made great progress in image and natural-language processing and has been applied in the classification of biomedical signals with good performance [ 12 , 13 , 14 ], especially for big data.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Sun

Xia

et al. 2023

Sensors

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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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