LASSO regression for monitoring patients progress following ACL reconstruction via motion sensors: a case study

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dc.contributor.authorTedesco, Salvatore
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-09-16T11:17:57Z
dc.date.available2019-09-16T11:17:57Z
dc.date.issued2019
dc.date.updated2019-09-16T11:10:33Z
dc.description.abstractInertial data can represent a rich source of clinically relevant information, which can provide details on motor assessment in subjects undertaking a rehabilitation process. Indeed, in clinical and sport settings, motor assessment is generally conducted through simple subjective measures such as a visual assessment or questionnaire given by caregivers. Thus, inertial sensor technology and associated data sets can help provide an objective and empirical measure of a patient’s progress. In this publication, several metrics in different domains have been considered and extrapolated from the three-dimensional accelerometer and angular rate data sets collected on an impaired subject with knee injury, via a wearable sensing system developed at the Tyndall National Institute. These data sets were collected for different activities performed across a number of sessions as the subject progressed through the rehabilitation process. Using these data sets and adopting a combination of techniques (LASSO, elastic net regularization, screening-based approaches, and leave-one-out cross-validation), an automated method has been defined in order to select the most suitable features which could provide accurate quantitative analysis of the improvement of the subject throughout their rehabilitation. The present work confirms that changes in motor ability can be objectively assessed via data-driven methods and that most of the alterations of interest occur on the sagittal plane and may be assessed by an accelerometer worn on the thigh.en
dc.description.sponsorshipEnterprise Ireland (EI under grant number CF-2017-0685-P)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTedesco, S. and O'Flynn, B. (2019) 'LASSO regression for monitoring patients progress following ACL reconstruction via motion sensors: a case study', International Journal on Advances in Life Sciences, 11 (1&2), pp. 23-32, http://www.iariajournals.org/life_sciences/en
dc.identifier.endpage32en
dc.identifier.issn1942-2660
dc.identifier.issued1&2en
dc.identifier.journaltitleInternational Journal on Advances in Life Sciencesen
dc.identifier.startpage23en
dc.identifier.urihttps://hdl.handle.net/10468/8533
dc.identifier.volume11en
dc.language.isoenen
dc.publisherIARIAen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.urihttp://www.iariajournals.org/life_sciences/lifsci_v11_n12_2019_paged.pdf
dc.rights2019, © Copyright by authors, Published under agreement with IARIA - www.iaria.orgen
dc.subjectRegressionen
dc.subjectFeature selectionen
dc.subjectMotor assessmenten
dc.subjectRehabilitationen
dc.subjectWearableen
dc.titleLASSO regression for monitoring patients progress following ACL reconstruction via motion sensors: a case studyen
dc.typeArticle (peer-reviewed)en
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