Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure.
Previous pattern recognition algorithms using surface electromyography (sEMG) have been developed for subsets of predefined hand movements without considering muscle structure. In order to decode hand movements, it is important to know which movements are appropriate for PR due to the different inde...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 8; pp. 1507 - 1519 |
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| Main Authors: | , , , , |
| Format: | algorithm equations & formulas pictorial research tables/charts tracings Journal Article |
| Published: |
Springer Nature
Aug2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124485671&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124485671 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2017 vid: 55 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124485671 124485671 143991491 NLM28054301 124485671 10.1007/s11517-016-1608-4 NLM28054301 124485671 ppf: 1507 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure. aug: au: Na, Youngjin Kim, Sangjoon Jo, Sungho Kim, Jung Kim, Sangjoon J affil: Department of Mechanical Engineering , Korea Advanced Institute of Science and Technology (KAIST) , Daejeon Republic of Korea sug: subj: Muscle Contraction Physiology Forearm Physiology Hand Physiology Information Science Methods Muscle, Skeletal Physiology Movement Physiology Electromyography Methods Reproducibility of Results Sensitivity and Specificity Adult Female Male Human Adult: 19-44 years Female Male ab: Previous pattern recognition algorithms using surface electromyography (sEMG) have been developed for subsets of predefined hand movements without considering muscle structure. In order to decode hand movements, it is important to know which movements are appropriate for PR due to the different independence of movements between individuals and the high correlated characteristics of sEMG patterns between movements. This paper proposes a method to personally rank the order of hand movements from subsets (31 finger flexion, 31 finger extension, and 4 wrist movements in this paper). The movements were sorted into a ranked order with respect to the locations of the electrodes on the proximal forearm and the distal forearm. We evaluated the classification error as the number of desired movements (N m) changed. The maximum N m with an error lower than 10% was 20 for the proximal forearm and 10 for the distal forearm from ranked movements of individuals. Our method could help to identify the optimized order of hand movements considering the personal characteristics of each individual. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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