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Accuracy of Hidden Markov Models in Identifying Alterations in Movement Patterns during Biceps-Curl Weight-Lifting Exercise

2022-12Journal article Open access
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dc.contributor.authorPeres, André B.
dc.contributor.authorEspada, Mário
dc.contributor.authorSantos, Fernando Jorge Lourenço Dos
dc.contributor.authorRobalo, Ricardo A. M.
dc.contributor.authorDias, Amândio A. P.
dc.contributor.authorMuñoz-Jiménez, Jesus
dc.contributor.authorSancassani, Andrei
dc.contributor.authorMassini, Danilo A.
dc.contributor.authorFilho, Dalton M. Pessôa
dc.date.accessioned2023-02-28T15:55:46Z
dc.date.available2023-02-28T15:55:46Z
dc.date.issued2022-12
dc.description.abstractThis paper presents a comparison of mathematical and cinematic motion analysis regarding the accuracy of the detection of alterations in the patterns of positional sequence during biceps-curl lifting exercise. Two different methods, one with and one without metric data from the environment, were used to identify the changes. Ten volunteers performed a standing biceps-curl exercise with additional loads. A smartphone recorded their movements in the sagittal plane, providing information on joints and barbell sequential position changes during each lift attempt. An analysis of variance revealed significant differences in joint position (p < 0.05) among executions with three different loads. Hidden Markov models were trained with data from the bi-dimensional coordinates of the joint positional sequence to identify meaningful alteration with load increment. Tests of agreement tests between the results provided by the models with the environmental measurements, as well as those from image coordinates, were performed. The results demonstrated that it is possible to efficiently detect changes in the patterns of positional sequence with and without the necessity of measurement and/or environmental control, reaching an agreement of 86% between each other, and 100% and 86% for each respective method to the results of ANOVA. The method developed in this study illustrates the viability of smartphone camera use for identifying positional adjustments due to the inability to control limbs in an adequate range of motion with increasing load during a lifting task.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPeres, A.B., Espada, M.C., Santos, F.J., Robalo, R.A.M., Dias, A.A.P., Muñoz-Jiménez, J., Sancassani, A., Massini, D.A., Pessôa Filho, D.M.(2023). Accuracy of Hidden Markov Models in Identifying Alterations in Movement Patterns during Biceps-Curl Weight-Lifting Exercise. Applied Sciences, 13, 573. https://doi.org/10.3390/ app13010573pt_PT
dc.identifier.doi10.3390/app13010573pt_PT
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.26/43976
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationLife Quality Research Centre
dc.relation.publisherversionhttps://www.mdpi.com/journal/applscipt_PT
dc.subjectPattern recognitionpt_PT
dc.subjectMotor activitypt_PT
dc.subjectTheoretical modelspt_PT
dc.subjectResistance trainingpt_PT
dc.titleAccuracy of Hidden Markov Models in Identifying Alterations in Movement Patterns during Biceps-Curl Weight-Lifting Exercisept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLife Quality Research Centre
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04748%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameEspada
person.familyNameSantos
person.givenNameMário
person.givenNameFernando Jorge Lourenço
person.identifierAAV-4731-2021
person.identifierY-7985-2018
person.identifier.ciencia-id9B1E-534A-F8CB
person.identifier.ciencia-idDD10-36B5-7F3D
person.identifier.orcid0000-0002-4524-4784
person.identifier.orcid0000-0002-1356-7853
person.identifier.scopus-author-id57226112719
person.identifier.scopus-author-id55348391900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationa1687ea2-bab6-482f-b67a-4bbaeed54be2
relation.isAuthorOfPublication4ec53a59-78fa-4b4c-b887-6943a7f6c21c
relation.isAuthorOfPublication.latestForDiscoverya1687ea2-bab6-482f-b67a-4bbaeed54be2
relation.isProjectOfPublication57d44aae-9fa2-4c4a-8b22-3221512dc75c
relation.isProjectOfPublication.latestForDiscovery57d44aae-9fa2-4c4a-8b22-3221512dc75c

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