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dc.contributor.authorMunoz-Organero, Mario
dc.contributor.authorLittlewood, Chris
dc.contributor.authorParker, Jack
dc.contributor.authorPowell, Lauren
dc.contributor.authorGrindell, Cheryl
dc.contributor.authorMawson, Sue
dc.date.accessioned2020-07-13T13:46:23Z
dc.date.available2020-07-13T13:46:23Z
dc.date.issued2017-06-15
dc.identifier.citationMunoz-Organero, M., Littlewood, C., Parker, J., Powell, L., Grindell, C. and Mawson, S., (2017). 'Identification of walking strategies of people with osteoarthritis of the knee using insole pressure sensors'. IEEE Sensors Journal, 17(12), pp. 3909-3920.en_US
dc.identifier.issn1530-437X
dc.identifier.doi10.1109/jsen.2017.2696303
dc.identifier.urihttp://hdl.handle.net/10545/624986
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_US
dc.description.sponsorshipHERMES-SMART DRIVERen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.urlhttps://ieeexplore.ieee.org/abstract/document/7906527en_US
dc.relation.urlhttp://eprints.whiterose.ac.uk/119146/en_US
dc.subjectInsole pressure sensors , mild knee pain , osteoarthritis , machine learning , classificationen_US
dc.titleIdentification of walking strategies of people With osteoarthritis of the knee using insole pressure sensorsen_US
dc.typeArticleen_US
dc.identifier.eissn2379-9153
dc.contributor.departmentCharles III University of Madrid, Madrid, Spainen_US
dc.contributor.departmentKeele Universityen_US
dc.contributor.departmentUniversity of Sheffielden_US
dc.identifier.journalIEEE Sensorsen_US
dc.source.journaltitleIEEE Sensors Journal
dc.source.volume17
dc.source.issue12
dc.source.beginpage3909
dc.source.endpage3920
dcterms.dateAccepted2017
dc.author.detail787041en_US


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