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Markov transition field for fall detection using time-series data

2025Comunicação em conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorKalbermatter, Rebeca B.
dc.contributor.authorSilva, Felipe G.
dc.contributor.authorPereira, Ana I.
dc.contributor.authorValente, António
dc.contributor.authorLima, José
dc.contributor.authorYahiaoui, Réda
dc.contributor.authorFayad, Moustafa
dc.date.accessioned2026-06-12T11:29:13Z
dc.date.available2026-06-12T11:29:13Z
dc.date.issued2025
dc.description.abstractFall detection systems have traditionally relied on sequential pattern recognition methods, using, for example, time series data obtained from inertial sensors, such as accelerometers. This paper proposes a methodology for fall detection based on converting time series from accelerometer sensors into visual representations using the Markov Transition Field (MTF) method. The UP-Fall dataset was used to test the performance of a Convolutional Neural Network (CNN) model trained on the MTF images generated. A systematic analysis of the image generation parameters was carried out, including the window size, the percentage of overlap, and the number of bins used in the discretizations. The experiments showed that the configuration with 55 bins, a window of 200 samples, and 40% overlap resulted in the best accuracy (97.13%), demonstrating that the conversion of sensory signals into MTF images is a promising alternative for fall detection, allowing computer vision models to capture relevant temporal patterns with high efficiency.eng
dc.description.sponsorshipThis work was supported by National funds: UID/05757 - Research Centre in Digitalization and Intelligent Robotics (CeDRI); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020) and the Junior Professor Chair (CPJ) of Franche Comte University, Galaxie number 4718 ANR-23-CPJ1-0010-01. Rebeca B. Kalbermatter is supported by the European Union under Horizon Europe (Project 101078933 - STEP - STEM and Equality, Diversity and Inclusion: an open dialogue for research enhancement in Portugal). Any related publications reflect only the views of the authors.
dc.identifier.citationKalbermatter, Rebeca B.; Silva, Felipe G.; Pereira, Ana I.; Valente, António; Lima, José; Yahiaoui, Réda; Fayad, Moustafa (2025). Markov transition field for fall detection using time-series data. In IEEE International Conference on E-health Networking, Applications and Services, IEEE HealthCom 2025. IEEE. p. 1-6. ISBN 979-833150989-7
dc.identifier.doi10.1109/healthcom60686.2025.11342661
dc.identifier.isbn979-833150989-7
dc.identifier.urihttp://hdl.handle.net/10198/36870
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationCentro de Investigação em Digitalização e Robótica - UID/05757/2025
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
dc.relation.ispartof2025 IEEE International Conference on E-health Networking, Application & Services (Healthcom)
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectFall detection
dc.subjectMarkov transition field
dc.subjectConvolutional neural network
dc.subjectTime series segmentation
dc.subjectSensor data
dc.titleMarkov transition field for fall detection using time-series dataeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUID/05757/2025
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleCentro de Investigação em Digitalização e Robótica - UID/05757/2025
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
oaire.awardURIhttps://doi.org/10.54499/UID/05757/2025
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferencePlaceAbu Dhabi
oaire.citation.endPage6
oaire.citation.startPage1
oaire.citation.titleIEEE International Conference on E-health Networking, Applications and Services, IEEE HealthCom 2025
oaire.fundingStreamAvaliação UID 2023/2024
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameKalbermatter
person.familyNamePereira
person.familyNameLima
person.givenNameRebeca B.
person.givenNameAna I.
person.givenNameJosé
person.identifierR-000-8GD
person.identifier.ciencia-idC410-B5A7-8C80
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0002-5219-4084
person.identifier.orcid0000-0003-3803-2043
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridF-3168-2010
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id15071961600
person.identifier.scopus-author-id55851941311
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
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