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Intelligent sports weights

2025-06-18Research article Open access
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authorProfile.emailbiblioteca@isel.pt
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorDuarte, Olga dos Santos
dc.contributor.authorJacinto, Gustavo
dc.contributor.authorVéstias, Mário
dc.contributor.authorVéstias, Mário
dc.contributor.authorDuarte, Rui Policarpo
dc.contributor.authorDuarte, Rui
dc.date.accessioned2025-07-10T12:24:14Z
dc.date.available2025-07-10T12:24:14Z
dc.date.issued2025-06-18
dc.description.abstractWeightlifting is a common fitness activity and can be practiced individually without supervision. However, performing regular weightlifting exercises without any form of feedback can lead to serious injuries. To counter this, this work proposes a different approach to automatic weightlifting supervision off-the-person. The proposed embedded system is coupled to the weights and evaluates if they follow the correct trajectory in real time. The system is based on a low-power embedded System-on-a-Chip to perform the classification of the correctness of physical exercises using a Convolutional Neural Network with data from the embedded IMU. It is a low-cost solution and can be adapted to the characteristics of specific exercises to fine-tune the performance of the athlete. Experimental results show real-time monitoring capability with an average accuracy close to 95%. To favor its use, the prototypes have been enclosed on a custom 3D case and validated in an operational environment. All research outputs, developments, and engineering models are publicly available.eng
dc.description.sponsorshipPL/IDI&CA2024/CSAT-OBC-ISEL - Instituto Politécnico de Lisboa
dc.identifier.citationDuarte, O. S., Jacinto, G., Véstias, M. & Duarte, R. P. (2025). Intelligent sports weights. Sensors, 25(12), 1-17. https://doi.org/10.3390/s25123808
dc.identifier.doihttps://doi.org/10.3390/s25123808
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.21/21969
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationhttps://doi.org/10.54499 /2023.15325.PEX
dc.relation.hasversionhttps://www.mdpi.com/1424-8220/25/12/3808
dc.relation.ispartofseries3808
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntellingentfitness
dc.subjectHuman activity recognition
dc.subjectLow-power
dc.subjectEmbedded IoT
dc.titleIntelligent sports weightseng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage17
oaire.citation.issue12
oaire.citation.startPage1
oaire.citation.titleSensors
oaire.citation.volume25
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameVéstias
person.familyNameDuarte
person.givenNameMário
person.givenNameRui
person.identifier.ciencia-id4717-C2C7-3F2C
person.identifier.ciencia-idB91E-770F-19A3
person.identifier.orcid0000-0001-8556-4507
person.identifier.orcid0000-0002-7060-4745
person.identifier.ridH-9953-2012
person.identifier.ridI-4402-2015
person.identifier.scopus-author-id14525867300
person.identifier.scopus-author-id24823991600
relation.isAuthorOfPublicationa7d22b29-c961-45ac-bc09-cd5e1002f1e8
relation.isAuthorOfPublicationf2b4b9e6-6c89-48c7-bc83-62d2e98a787b
relation.isAuthorOfPublication.latestForDiscoverya7d22b29-c961-45ac-bc09-cd5e1002f1e8

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