A neural network approach for assessing the relationship between grip strength and hand anthropometry

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dc.contributor.authorÇakıt, Erman
dc.contributor.authorDurgun, Behice
dc.contributor.authorCetik, Oya
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T08:22:13Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T08:22:13Z
dc.date.issued2015
dc.departmentMühendislik Fakültesi
dc.description.abstractThis study aimed to determine grip strength data for Turkish dentistry students and developed prediction models that allow: i) investigation of the rela- tionship between grip strength and hand anthropometry using artificial neural net- works (ANNs) and stepwise regression analysis, ii) prediction of the grip strength of Turkish dentistry students, and iii) assessment of the potential impact of hand anthropometric variables on grip strength. The study included 153 right-handed dentistry students, consisting of 81 males and 72 females. From 44 anthropometric and biomechanical measurements obtained from the right hands of the participants; five anthropometric measurements were selected for ANN and regression modeling using stepwise regression analysis. We included stepwise regression analysis results to assess the predictive power of the neural network approach, in comparison to a classical statistical approach. When the model accuracy was calculated based on the coefficient of determination (R2), the root mean squared error (RMSE) and the mean absolute error (MAE) values for each of the models, ANN showed greater pre- dictive accuracy than regression analysis, as demonstrated by experimental results. For the best performing ANN model, the testing values of the models correlated well with actual values, with a coefficient of determination (R2) of 0.858. Using the best performing ANN model, sensitivity analysis was applied to determine the effects of hand dimensions on grip strength and to rank these dimensions in order of importance. The results suggest that the three most sensitive input variables are the forearm length, the hand breadth and the finger circumference at the first joint of digit 5 and that the ANNs are promising techniques for predicting hand grip strength based on hand breadth, finger breadth, hand length, finger circumference and forearm length. © CTU FTS 2015.
dc.identifier.doi10.14311/NNW.2015.25.030
dc.identifier.endpage622en_US
dc.identifier.issn1210-0552
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ4
dc.identifier.startpage603en_US
dc.identifier.urihttps://dx.doi.org/10.14311/NNW.2015.25.030
dc.identifier.urihttps://hdl.handle.net/20.500.12451/2502
dc.identifier.volume25en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Computer Science
dc.relation.ispartofNeural Network World
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Neural Network
dc.subjectGrip Strength
dc.subjectHand Dimensions
dc.subjectSensitivity Analysis
dc.subjectStepwise Regression Analysis
dc.titleA neural network approach for assessing the relationship between grip strength and hand anthropometry
dc.typeArticle

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