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Assessing gait impairments based on auto-encoded patterns of mahalanobis distances from consecutive steps

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorMuñoz Organero, Marioes
dc.contributor.authorDavies, Richardes
dc.contributor.authorMawson, Sueen
dc.date.accessioned2018-03-21T11:53:33Z
dc.date.available2018-03-21T11:53:33Z
dc.date.issued2017
dc.descriptionProceedings of: 14th conference of the Association for the Advancement of Assistive Technology in Europe (AAATE 2017), Sheffield (UK), 12-15th September 2017.en
dc.description.abstractInsole pressure sensors capture the force distribution patterns during the stance phase while walking. By comparing patterns obtained from healthy individuals to patients suffering different medical conditions based on a given similarity measure, automatic impairment indexes can be computed in order to help in applications such as rehabilitation. This paper uses the data sensed from insole pressure sensors for a group of healthy controls to train an auto-encoder using patterns of stochastic distances in series of consecutive steps while walking at normal speeds. Two experiment groups are compared to the healthy control group: a group of patients suffering knee pain and a group of post-stroke survivors. The Mahalanobis distance is computed for every single step by each participant compared to the entire dataset sensed from healthy controls. The computed distances for consecutive steps are fed into the previously trained autoencoder and the average error is used to assess how close the walking segment is to the autogenerated model from healthy controls. The results show that automatic distortion indexes can be used to assess each participant as compared to normal patterns computed from healthy controls. The stochastic distances observed for the group of stroke survivors are bigger than those for the people with knee pain.en
dc.description.sponsorshipThe research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).en
dc.format.extent7es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationPeter Cudd, Luc de Witte (eds.) (2014). Harnessing the power of technology to improve lives. (Studies in Health Technology and Informatics, 242). Amsterdam: IOS Press, 2017. Pp. 733-740.en
dc.identifier.doihttps://doi.org/10.3233/978-1-61499-798-6-733
dc.identifier.isbn978-1-61499-797-9 (Print)
dc.identifier.isbn978-1-61499-798-6 (Online)
dc.identifier.issn0926-9630 (Print)
dc.identifier.issn1879-8365 (Online)
dc.identifier.publicationfirstpage733es
dc.identifier.publicationlastpage740es
dc.identifier.publicationtitleHarnessing the power of technology to improve livesen
dc.identifier.urihttps://hdl.handle.net/10016/26515
dc.identifier.uxxiCC/0000027441
dc.language.isoengen
dc.publisherIOS Pressen
dc.relation.eventdate12-15, septiembre 2017es
dc.relation.eventnumber14
dc.relation.eventplaceSheffield (UK)en
dc.relation.eventtitle14th conference of the Association for the Advancement of Assistive Technology in Europe (AAATE 2017)en
dc.relation.ispartofseriesStudies in Health Technology and Informaticsen
dc.relation.ispartofseries242es
dc.relation.projectIDGobierno de España. TIN2013-46801-C4-2-Res
dc.relation.projectIDGobierno de España. TIN2016-77158-C4-1-Res
dc.rights© 2017 The authors and IOS Press.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaMedicinaes
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherGait impairmenten
dc.subject.otherPattern analysisen
dc.subject.otherModellingen
dc.subject.otherMahalanobis distanceen
dc.titleAssessing gait impairments based on auto-encoded patterns of mahalanobis distances from consecutive stepsen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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