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dc.contributor.authorAbaid, Nicoleen
dc.contributor.authorCappa, Paoloen
dc.contributor.authorPalermo, Eduardoen
dc.contributor.authorPetrarca, Maurizioen
dc.contributor.authorPorfiri, Maurizioen
dc.date.accessioned2018-10-18T16:58:14Zen
dc.date.available2018-10-18T16:58:14Zen
dc.date.issued2013-09-04en
dc.identifier.othere73152en
dc.identifier.urihttp://hdl.handle.net/10919/85408en
dc.description.abstractIn this work, we develop a novel gait phase detection algorithm based on a hidden Markov model, which uses data from foot-mounted single-axis gyroscopes as input. We explore whether the proposed gait detection algorithm can generate equivalent results as a reference signal provided by force sensitive resistors (FSRs) for typically developing children (TD) and children with hemiplegia (HC). We find that the algorithm faithfully reproduces reference results in terms of high values of sensitivity and specificity with respect to FSR signals. In addition, the algorithm distinguishes between TD and HC and is able to assess the level of gait ability in patients. Finally, we show that the algorithm can be adapted to enable real-time processing with high accuracy. Due to the small, inexpensive nature of gyroscopes utilized in this study and the ease of implementation of the developed algorithm, this work finds application in the on-going development of active orthoses designed for therapy and locomotion in children with gait pathologies.en
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleGait Detection in Children with and without Hemiplegia Using Single-Axis Wearable Gyroscopesen
dc.typeArticle - Refereeden
dc.description.versionPeer Revieweden
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.title.serialPLOS ONEen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0073152en
dc.identifier.volume8en
dc.identifier.issue9en
dc.type.dcmitypeTexten
dc.identifier.pmid24023825en
dc.identifier.eissn1932-6203en


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International