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Recognition of Physiotherapeutic Exercises Through DTW and Low-Cost Vision-Based Motion Capture

2018-01-01Documento de conferência Acesso aberto
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dc.contributor.authorRybarczyk, Yves
dc.contributor.authorDeters, Jan Kleine
dc.contributor.authorGonzalo, Arián Aladro
dc.contributor.authorEsparza, Danilo
dc.contributor.authorGonzalez, Mario
dc.contributor.authorVillarreal, Santiago
dc.contributor.authorNunes, Isabel L.
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.institutionDEE2010-C2 Robótica e Manufactura Integrada por Computador
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.coverage.spatialCham
dc.date.accessioned2020-01-03T23:24:17Z
dc.date.available2020-01-03T23:24:17Z
dc.date.issued2018-01-01
dc.description.abstractTelemedicine is a current trend in healthcare. The present study is part of the ePHoRt project, which is a web-based platform for the rehabilitation of patients after hip replacement surgery. To be economically suitable the system is intended to be based on low-cost technologies, especially in terms of motion capture. This is the reason why the Kinect-based motion tracking is chosen. The paper focuses on the automatic assessment of the correctness of the exercises performed by the user. A Dynamic Time Warping (DTW) approach is used to discriminate between correct and incorrect movements. The classification of the movements through a Naïve Bayes classifier shows a very high percentage of accuracy (98.2%). Models are built for each individual and reeducation exercise with only few attributes and the same accuracy. Due to these promising results, the next step will consist of testing the algorithms on patients performing the exercises in real time.en
dc.description.versionpublished
dc.format.extent13
dc.format.extent657126
dc.identifier.doi10.1007/978-3-319-60366-7_33
dc.identifier.isbn978-3-319-60365-0
dc.identifier.isbn978-3-319-60366-7
dc.identifier.issn2194-5357
dc.identifier.otherPURE: 3698998
dc.identifier.otherPURE UUID: 98e307b0-8da6-4e27-bfe0-7d26e1a24e34
dc.identifier.otherScopus: 85041131278
dc.identifier.otherORCID: /0000-0002-0428-0930/work/50464885
dc.identifier.otherWOS: 000448246000033
dc.identifier.urihttp://hdl.handle.net/10362/90632
dc.identifier.urlhttps://www.scopus.com/pages/publications/85041131278
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.subjectDynamic Time Warping
dc.subjectKinect-based motion tracking
dc.subjectMachine learning
dc.subjectMovement assessment
dc.subjectTelerehabilitation
dc.subjectControl and Systems Engineering
dc.subjectGeneral Computer Science
dc.titleRecognition of Physiotherapeutic Exercises Through DTW and Low-Cost Vision-Based Motion Captureen
dc.typeconference object
degois.publication.firstPage348
degois.publication.lastPage360
degois.publication.titleAdvances in Human Factors and Systems Interaction - Proceedings of the AHFE 2017 International Conference on Human Factors and Systems Interaction, 2017
degois.publication.titleAHFE 2017 International Conference on Human Factors and Systems Interaction, 2017
dspace.entity.typePublication
rcaap.rightsrestrictedAccess

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