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dc.contributor.authorOjeda, Maribel
dc.contributor.authorCortés Martínez, Atia
dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2018-09-28T08:21:45Z
dc.date.available2018-09-28T08:21:45Z
dc.date.issued2018
dc.identifier.citationOjeda, M., Cortés , A., Béjar, J., Cortés, U. Automatic classification of gait patterns using a smart rollator and the BOSS model. A: PErvasive Technologies Related to Assistive Environments Conference. "PETRA '18 Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference". New York: Association for Computing Machinery (ACM), 2018, p. 384-390.
dc.identifier.isbn978-1-4503-6390-7
dc.identifier.urihttp://hdl.handle.net/2117/121595
dc.description.abstractNowadays, the risk of falling in older adults is a major concern due to the severe consequences it brings to socio-economic and public health systems. Some pathologies cause mobility problems in the aged population, leading them to fall and, thus, reduce their autonomy. Other implications of ageing involve having different gait patterns and walking speed. In this paper, a non-invasive framework is proposed to study gait in elder people using data collected by a smart rollator, the i-Walker. The analysis presented in this article uses a feature extraction method and a spectral embedding to represent the information and Bayesian clustering for the knowledge discovery. The algorithm considers raw data from the i-Walker sensors along with the calculated walking speed of each individual, which has been already used in clinical studies to assess physical and cognitive status of older adults. The results obtained demonstrate that the proposed analysis has the potential to separate in clusters the people of the two groups of interest: young people and geriatric.
dc.format.extent7 p.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMedical informatics
dc.subject.lcshMachine learning
dc.subject.lcshSelf-help devices for people with disabilities
dc.subject.otherGait analysis
dc.subject.otherAssistive technologies
dc.subject.otherTime series clustering
dc.titleAutomatic classification of gait patterns using a smart rollator and the BOSS model
dc.typeConference lecture
dc.subject.lemacMedicina -- Informàtica
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAjuts tecnològics per als discapacitats
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.1145/3197768.3201575
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?id=3201575&dl=ACM&coll=DL
dc.rights.accessOpen Access
drac.iddocument23349933
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2014-56256-C2-2-P
upcommons.citation.authorOjeda, M.; Cortés, A.; Béjar, J.; Cortés, U.
upcommons.citation.contributorPErvasive Technologies Related to Assistive Environments Conference
upcommons.citation.pubplaceNew York
upcommons.citation.publishedtrue
upcommons.citation.publicationNamePETRA '18 Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
upcommons.citation.startingPage384
upcommons.citation.endingPage390


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