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Identification of walking strategies of people with osteoarthritis of the knee using insole pressure sensors

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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, Mario
dc.contributor.authorLittlewood, Chris
dc.contributor.authorParker, Jack
dc.contributor.authorPowell, Lauren
dc.contributor.authorGrindell, Cheryl
dc.contributor.authorMawson, Sue
dc.date.accessioned2018-03-22T11:47:40Z
dc.date.available2018-03-22T11:47:40Z
dc.date.issued2017-06-15
dc.description.abstractInsole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up. Using data analysis and machine learning techniques, common patterns and strategies from different users to execute different tasks can be extracted. In this paper, we present the evaluation results of the impact that clinically diagnosed osteoarthritis of the knee at early stages has on insole pressure sensors while walking at normal speeds focusing on the effects caused at points, where knee forces tend to peak for normal users. From the different parts of the foot affected at high knee force moments, the forefoot pressure distribution and the heel to forefoot weight reallocation strategies have shown to provide better correlations with the user's perceived pain in the knee for OA users with mild knee pain. This paper shows how the time differences and variabilities from two sensors located in the metatarsal zone while walking provide a simple mechanism to detect different strategies used by users suffering OA of the knee from control users with no knee pain. The weight dynamic reallocation at the midfoot, when moving forward from heel to forefoot, has also shown to positively correlate with the perceived knee pain. The major asymmetries between pressure patterns in both feet while walking at normal speeds are also captured. Based on the described features, automatic evaluation self-management rehabilitation tools could be implemented to continuously monitor and provide personalized feedback for OA patients with mild knee pain to facilitate user adherence to individualized OA rehabilitation.en
dc.description.sponsorshipThis work was supported in part by the HERMES-SMART DRIVER Project under Grant TIN2013-46801-C4-2-R (MINECO), in part by the Spanish Agencia Estatal de Investigación (AEI), in part by the ANALYTICS USING SENSOR DATA FOR FLATCITY Project through the AEI under Grant TIN2016-77158-C4-1-R (MINECO/ERDF, EU), in part by the European Regional Development Fund, in part by the Ministerio de Educación Cultura y Deporte under Grant PRX15/00036en
dc.format.extent11es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationIEEE Sensors Journal, (2017), 17(12), 3909-3920.en
dc.identifier.doihttps://doi.org/10.1109/JSEN.2017.2696303
dc.identifier.issn1530-437X
dc.identifier.publicationfirstpage3909es
dc.identifier.publicationissue12es
dc.identifier.publicationlastpage3920es
dc.identifier.publicationtitleIEEE Sensors Journalen
dc.identifier.publicationvolume17es
dc.identifier.urihttps://hdl.handle.net/10016/26538
dc.identifier.uxxiAR/0000020706
dc.language.isoenges
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TIN2013-46801-C4-2-Res
dc.relation.projectIDGobierno de España. TIN2016-77158-C4-1-Res
dc.relation.projectIDGobierno de España. PRX15/00036es
dc.rights© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en
dc.subject.ecienciaMedicinaes
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherInsole pressure sensorsen
dc.subject.otherOsteoarthritisen
dc.subject.otherMachine learningen
dc.subject.otherClassificationen
dc.titleIdentification of walking strategies of people with osteoarthritis of the knee using insole pressure sensorsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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