Classification of high knee flexion postures using EMG signals.
BACKGROUND: High knee flexion postures are often adopted in occupational settings and may lead to increased risk of knee osteoarthritis. Pattern recognition algorithms using wireless electromyographic (EMG) signals may be capable of detecting and quantifying occupational exposures throughout a worki...
| Publicado en: | Work Vol. 68; no. 3; pp. 701 - 710 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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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=160235308&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160235308 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2021 vid: 68 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 160235308 148853057 160235308 160235308 10.3233/WOR-203404 160235308 ppf: 701 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of high knee flexion postures using EMG signals. aug: au: Laudanski, Annemarie F. Acker, Stacey M. affil: Department of Kinesiology, Faculty of Health, University of Waterloo, Waterloo, ON, Canada sug: subj: Body Mechanics Evaluation Knee Joint Physiology Electromyography Utilization Posture Flexion Human Algorithms Male Female Adult Descriptive Statistics Funding Source Occupational Exposure Adverse Effects Osteoarthritis Risk Factors Osteoarthritis Diagnosis Adult: 19-44 years Male Female ab: BACKGROUND: High knee flexion postures are often adopted in occupational settings and may lead to increased risk of knee osteoarthritis. Pattern recognition algorithms using wireless electromyographic (EMG) signals may be capable of detecting and quantifying occupational exposures throughout a working day. OBJECTIVE: To develop a k-Nearest Neighbor (kNN) algorithm for the classification of eight high knee flexion activities frequently observed in childcare. METHODS: EMG signals from eight lower limb muscles were recorded for 30 participants, signals were decomposed into time- and frequency-domain features, and used to develop a kNN classification algorithm. Features were reduced to a combination of ten time-domain features from 8 muscles using neighborhood component analysis, in order to most effectively identify the postures of interest. RESULTS: The final classifier was capable of accurately identifying 80.1%of high knee flexion postures based on novel data from participants included in the training dataset, yet only achieved 18.4%accuracy when predicting postures based on novel subject data. CONCLUSIONS: EMG based classification of high flexion postures may be possible within occupational settings when the model is first trained on sample data from a given individual. The developed algorithm may provide quantitative measures leading to a greater understanding of occupation specific postural requirements. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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