Assessing walking strategies using insole pressure sensors for stroke survivors

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dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Parker, Jack
dc.contributor.author Powell, Lairen
dc.contributor.author Mawson, Susan
dc.date.accessioned 2018-02-22T15:57:48Z
dc.date.available 2018-02-22T15:57:48Z
dc.date.issued 2016-10-01
dc.identifier.bibliographicCitation Munoz-Organero, M., Parker, J., Powell, L., Mawson, S. (2016). Assessing Walking Strategies Using Insole Pressure Sensors for Stroke Survivors. Sensors, 16 (10), 1631.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/25573
dc.description.abstract Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up such as walking. Using data analysis and machine learning techniques, common patterns and strategies from different users to achieve different tasks can be automatically extracted. In this paper, we present the results obtained for the automatic detection of different strategies used by stroke survivors when walking as integrated into an Information Communication Technology (ICT) enhanced Personalised Self-Management Rehabilitation System (PSMrS) for stroke rehabilitation. Fourteen stroke survivors and 10 healthy controls have participated in the experiment by walking six times a distance from chair to chair of approximately 10 m long. The Rivermead Mobility Index was used to assess the functional ability of each individual in the stroke survivor group. Several walking strategies are studied based on data gathered from insole pressure sensors and patterns found in stroke survivor patients are compared with average patterns found in healthy control users. A mechanism to automatically estimate a mobility index based on the similarity of the pressure patterns to a stereotyped stride is also used. Both data gathered from stroke survivors and healthy controls are used to evaluate the proposed mechanisms. The output of trained algorithms is applied to the PSMrS system to provide feedback on gait quality enabling stroke survivors to self-manage their rehabilitation.
dc.description.sponsorship The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte. The research was also funded and supported by the NIHR CLAHRC Yorkshire and Humber.
dc.format.extent 18
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI (Multidisciplinary Digital Publishing Institute)
dc.rights © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/)
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Insole pressure sensors
dc.subject.other Stroke survivals
dc.subject.other Machine learning
dc.subject.other Rehabilitation
dc.subject.other Walking strategies
dc.subject.other Self-management
dc.subject.other Chronic disease
dc.subject.other Recovery
dc.subject.other Plasticity
dc.title Assessing walking strategies using insole pressure sensors for stroke survivors
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3390/s16101631
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2013-46801-C4-2-R
dc.relation.projectID Gobierno de España. PRX15/00036
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 10 (1631)
dc.identifier.publicationlastpage 18
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 16
dc.identifier.uxxi AR/0000018378
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