Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications.
Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition tec...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 5; pp. 747 - 759 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123106935&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123106935 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2017 vid: 55 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 123106935 123106935 NLM27484411 123106935 10.1007/s11517-016-1551-4 NLM27484411 123106935 ppf: 747 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications. aug: au: AL-Quraishi, Maged Ishak, Asnor Ahmad, Siti Hasan, Mohd Al-Qurishi, Muhammad Ghapanchizadeh, Hossein Alamri, Atif Al-Quraishi, Maged S Ishak, Asnor J Ahmad, Siti A Hasan, Mohd K affil: Department of Electrical and Electronic Engineering , Universiti Putra Malaysia , 43400 Serdang Malaysia sug: subj: Movement Physiology Ankle Joint Physiology Information Science Methods Discriminant Analysis Electromyography Methods Robotics Methods Female Signal Processing, Computer Assisted Adult Prostheses and Implants Male Algorithms Probability Human Adult: 19-44 years Female Male ab: Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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