Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities

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dc.contributor.authorFRANCISCO, J. A.
dc.contributor.authorPaulo Rodrigues
dc.contributor.authorOrcidhttps://orcid.org/0000-0003-3258-0794
dc.date.accessioned2023-01-01T06:03:06Z
dc.date.available2023-01-01T06:03:06Z
dc.date.issued2022-01-05
dc.description.abstractAuthorPhysical rehabilitation techniques during the treatment of clinical pathology are one of the most challenging areas for the medical structure, patients, and families. In large and continental countries, remote monitoring of this treatment is essential. However, equipment and medical follow-up during exercises still have high costs. With the improvement of computer vision and machine learning techniques, some computational, less expensive alternatives have been proposed in the literature. However, monitoring patients during physical rehabilitation exercises with the help of artificial intelligence by a health professional, especially from the capture of visual signals, is still a challenge and poorly explored in the scientific-technological literature. This work aims to propose a new methodology based on computer vision and machine learning for remote tracking of the body joints of patients during physiotherapy rehabilitation exercises. As a new contribution, this work presents a modular neural network architecture composed of two modules: one for detecting physical exercises and another for measuring how much is correct. Another contribution is a strategy for expanding databases, considering that generic databases for this type of exercise are rare on the internet. The results showed that both modules obtained more than 90% of accuracy in recognition and their respective validation.
dc.identifier.citationFRANCISCO, J. A.; RODRIGUES, P. S. Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities. IEEE Transactions on Neural Systems and Rehabilitation Engineering, p.1, 2022.
dc.identifier.doi10.1109/TNSRE.2022.3226459
dc.identifier.issn1558-0210
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4670
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.rightsAcesso Aberto
dc.subject.otherlanguageModular Neural Network
dc.subject.otherlanguageOpenPose
dc.subject.otherlanguagePhysical Therapy
dc.titleComputer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities
dc.typeArtigo
fei.scopus.citations1
fei.scopus.eid2-s2.0-85144011758
fei.scopus.subjectAutomatic assessment
fei.scopus.subjectMedical structures
fei.scopus.subjectModular neural networks
fei.scopus.subjectOpenpose
fei.scopus.subjectPhysical rehabilitation
fei.scopus.subjectRehabilitation activities
fei.scopus.subjectRehabilitation exercise
fei.scopus.subjectRehabilitation techniques
fei.scopus.subjectVision based
fei.scopus.subjectVision learning
fei.scopus.updated2024-05-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144011758&origin=inward
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