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

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dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Littlewood, Chris
dc.contributor.author Parker, Jack
dc.contributor.author Powell, Lauren
dc.contributor.author Grindell, Cheryl
dc.contributor.author Mawson, Sue
dc.date.accessioned 2018-03-22T11:47:40Z
dc.date.available 2018-03-22T11:47:40Z
dc.date.issued 2017-06-15
dc.identifier.bibliographicCitation IEEE Sensors Journal, (2017), 17(12), 3909-3920.
dc.identifier.issn 1530-437X
dc.identifier.uri http://hdl.handle.net/10016/26538
dc.description.abstract Insole 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.
dc.description.sponsorship This 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 PRX15/00036
dc.format.extent 11
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
dc.subject.other Insole pressure sensors
dc.subject.other Osteoarthritis
dc.subject.other Machine learning
dc.subject.other Classification
dc.title Identification of walking strategies of people with osteoarthritis of the knee using insole pressure sensors
dc.type article
dc.subject.eciencia Medicina
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/JSEN.2017.2696303
dc.relation.projectID Gobierno de España. TIN2013-46801-C4-2-R
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.relation.projectID Gobierno de España. PRX15/00036
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 3909
dc.identifier.publicationissue 12
dc.identifier.publicationlastpage 3920
dc.identifier.publicationtitle IEEE Sensors Journal
dc.identifier.publicationvolume 17
dc.identifier.uxxi AR/0000020706
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