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Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal

2024Conference paper Restricted access
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dc.contributor.authorBarbosa, Ana Carolina
dc.contributor.authorFerreira, Edilson Santos
dc.contributor.authorGrilo, Vinicius F.S.B.
dc.contributor.authorMattos, Laercio
dc.contributor.authorLima, José
dc.date.accessioned2024-10-08T13:31:33Z
dc.date.available2024-10-08T13:31:33Z
dc.date.issued2024
dc.description.abstractOur current society faces challenges in integrating individuals with disabilities, making this process difficult and painful. People with disabilities (PwD) are often mistakenly considered incapable due to the difficulties they face in daily tasks due to the lack of adapted means and tools. In this context, assistive technologies play a crucial role in improving the quality of life for these individuals. However, assistive technologies still have various limitations, making research in this area essential to enhance existing solutions and develop new approaches that meet individual needs, aiming to promote inclusion and equal opportunities. This paper presents a research project that focuses on the study of electromyography (EMG) signal processing generated by individuals who have undergone amputations. These signals are essential in assistive technologies, such as myoelectric prostheses. The study focuses on the impact of different filters and machine learning training methods on this processing. The results of this study have the potential to provide relevant findings for the development of more efficient assistive technologies. By understanding the processing of EMG signals and applying machine learning techniques, it is possible to improve the accuracy and response speed of prosthetics, increasing the functionality and naturalness of movements performed by users, as well as paving the way for the emergence of new technologies.pt_PT
dc.description.sponsorshipThe authors are grateful to CeDRI (UIDB/05757/2020, UIDP/05757/2020), SusTEC (LA/P/0007/2021) and SmartHealth (NORTE-01-0145- FEDER-000045).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBarbosa, Ana; Ferreira, Edilson; Grilo, Vinicius; Mattos, Laercio; Lima, José (2024). Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 50–62. ISBN 978-3-031-53035-7pt_PT
dc.identifier.doi10.1007/978-3-031-53036-4_15pt_PT
dc.identifier.isbn978-3-031-53035-7
dc.identifier.isbn978-3-031-53036-4
dc.identifier.urihttp://hdl.handle.net/10198/30374
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAssistive technologiespt_PT
dc.subjectElectromyography (EMG) signal processingpt_PT
dc.subjectMachine learningpt_PT
dc.titleImpact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signalpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage228pt_PT
oaire.citation.startPage211pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLima
person.givenNameJosé
person.identifierR-000-8GD
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id55851941311
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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