Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study.
In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape model...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 8; pp. 673 - 685 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2014
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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=103834552&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103834552 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2014 vid: 52 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103834552 NLM24961179 2012651625 10.1007/s11517-014-1170-x NLM24961179 103834552 ppf: 673 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study. aug: au: Ayachi, F S Boudaoud, S Marque, C affil: Multimodal Interaction Laboratory, SIS-McGill University, Montreal, Canada, sofiane.ayachi@mail.mcgill.ca. sug: subj: Computer Simulation Electromyography Methods Muscles Physiology Probability Action Potentials Physiology Kinematics ab: In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG-force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80% MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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