Hand movements classification for myoelectric control system using adaptive resonance theory

Fariman, Hessam Jahani; Ahmad, Siti Anom; Marhaban, Mohammad Hamiruce; Ghasab, M. Alijan; Chappell, P.H.

doi:10.1007/s13246-015-0399-5

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…Because TD feature extraction is easy to compute and use it can achieve satisfactory classification results in many cases. In some other studies, the representative TD features, such as MAV, ZC, RMS, were used with high accuracy [ 37 , 38 ]. However, TD features alone may not use sufficient signal information; thus, sometimes they are combined with other features [ 39 ].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%