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Similarity Index Values in Fuzzy Logic and the Support Vector Machine Method Applied to the Identification of Changes in Movement Patterns During Biceps-Curl Weight-Lifting Exercise

datacite.subject.fosCiências Médicas::Outras Ciências Médicas
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorPeres, A.B.
dc.contributor.authorAlmeida, T.A.F.
dc.contributor.authorMassini, D.A.
dc.contributor.authorMacedo,A.G.
dc.contributor.authorEspada, M.C.
dc.contributor.authorRobalo, R.A.M.;
dc.contributor.authorOliveira, Rafael
dc.contributor.authorBrito, J.P.
dc.contributor.authorPessôa Filho, D.M.
dc.contributor.authorOliveira, Rafael
dc.date.accessioned2025-04-13T12:48:45Z
dc.date.available2025-04-13T12:48:45Z
dc.date.issued2025-03
dc.description.abstractBackground/Objectives: Correct supervision during the performance of resis tance exercises is imperative to the correct execution of these exercises. This study presents a proposal for the use of Morisita–Horn similarity indices in modelling with machine learning methods to identify changes in positional sequence patterns during the biceps-curl weight-lifting exercise with a barbell. The models used are based on the fuzzy logic (FL) and support vector machine (SVM) methods. Methods: Ten male volunteers (age: 26 ±4.9 years, height: 177 ± 8.0cm, bodyweight: 86 ± 16kg)performedastandingbarbell bicep curl with additional weights. A smartphone was used to record their movements in the sagittal plane, providing information about joint positions and changes in the se quential position of the bar during each lifting attempt. Maximum absolute deviations of movement amplitudes were calculated for each execution. Results: A variance analysis revealed significant deviations (p < 0.002) in vertical displacement between the standard execution and execution with a load of 50% of the subject’s body weight. Experts with over thirty years of experience in resistance-exercise evaluation evaluated the exercises, and their results showed an agreement of over 70% with the results of the ANOVA. The similarity indices, absolute deviations, and expert evaluations were used for modelling in both the FL system and the SVM. The root mean square error and R-squared results for the FL system (R2 = 0.92, r = 0.96) were superior to those of the SVM (R2 = 0.81, r = 0.79). Conclusions: The use of FL in modelling emerges as a promising approach with which to support the assessment of movement patterns. Its applications range from automated detection of errors in exercise execution to enhancing motor performance in athletes.eng
dc.identifier.citationPeres, A. B., Almeida, T. A. F., Massini, D. A., Macedo, A. G., Espada, M. C., Robalo, R. A. M., Oliveira, R., Brito, J. P., & Pessôa Filho, D. M. (2025). Similarity Index Values in Fuzzy Logic and the Support Vector Machine Method Applied to the Identification of Changes in Movement Patterns During Biceps-Curl Weight-Lifting Exercise. Journal of Functional Morphology and Kinesiology, 10(1), 84. https://doi.org/10.3390/jfmk10010084
dc.identifier.doihttps://doi.org/10.3390/jfmk10010084
dc.identifier.issn2411-5142
dc.identifier.urihttp://hdl.handle.net/10400.15/5753
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/2411-5142/10/1/84
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectpattern recognition
dc.subjectmotor activity
dc.subjecttheoretical models
dc.subjectresistance training
dc.titleSimilarity Index Values in Fuzzy Logic and the Support Vector Machine Method Applied to the Identification of Changes in Movement Patterns During Biceps-Curl Weight-Lifting Exerciseeng
dc.typecontribution to journal
dspace.entity.typePublication
oaire.citation.titleJournal of Functional Morphology and Kinesiology
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameOliveira
person.givenNameRafael
person.identifier.ciencia-id9C16-7F53-1375
person.identifier.orcid0000-0001-6671-6229
relation.isAuthorOfPublication048765fe-a23f-4a68-9e6e-f278321223ac
relation.isAuthorOfPublication.latestForDiscovery048765fe-a23f-4a68-9e6e-f278321223ac

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