Estimating electromyography responses using an adaptive neuro-fuzzy inference system with subtractive clustering

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dc.contributor.authorÇakıt, Erman
dc.contributor.authorKarwowski, Waldemar
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:15:23Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:15:23Z
dc.date.issued2017
dc.departmentMühendislik Fakültesi
dc.description.abstractThis study aimed to develop an adaptive neuro-fuzzy inference system (ANFIS) approach to estimate the normalized electromyography (NEMG) responses, where the independent variables are demographic variables including population, gender, ethnicity, age, height, weight, posture, and muscle groups. The study groups comprised 75 US-based (54 males and 21 females) and 10 Japan-based (all males) automobile assembly workers. A total of 65 inputs and 1 output reflecting the NEMG values were considered at the beginning. After correlating analysis results, a total of 35 significant predictors were considered for both ANFIS and regression models. The data were partitioned into two datasets, one for training (70% of all data) and one for validation (30% of all data). In addition to a soft-computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANFIS approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, ANFIS had better predictive accuracy than MLR, as demonstrated by the experimental results. Overall, this study demonstrates that the ANFIS approach can predict normalized EMG responses according to subjects' demographic variables, posture, and muscle groups.
dc.identifier.doi10.1002/hfm.20701
dc.identifier.endpage186en_US
dc.identifier.issn1090-8471
dc.identifier.issn1520-6564
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage177en_US
dc.identifier.urihttps://doi.org/10.1002/hfm.20701
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4371
dc.identifier.volume27en_US
dc.identifier.wosWOS:000403806300002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofHuman Factors and Ergonomics in Manufacturing & Service Industries
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectElectromyography
dc.subjectLinear Regression
dc.subjectMuscular Efforts
dc.subjectNeuro-fuzzy Model
dc.subjectPosture
dc.titleEstimating electromyography responses using an adaptive neuro-fuzzy inference system with subtractive clustering
dc.typeArticle

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