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

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dc.contributor.author Tedesco, Salvatore
dc.contributor.author O'Flynn, Brendan
dc.date.accessioned 2019-09-16T11:17:57Z
dc.date.available 2019-09-16T11:17:57Z
dc.date.issued 2019
dc.identifier.citation Tedesco, 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.volume 11 en
dc.identifier.issued 1&2 en
dc.identifier.startpage 23 en
dc.identifier.endpage 32 en
dc.identifier.issn 1942-2660
dc.identifier.uri http://hdl.handle.net/10468/8533
dc.description.abstract Inertial 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.sponsorship Enterprise Ireland (EI under grant number CF-2017-0685-P) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher IARIA en
dc.relation.uri http://www.iariajournals.org/life_sciences/lifsci_v11_n12_2019_paged.pdf
dc.rights 2019, © Copyright by authors, Published under agreement with IARIA - www.iaria.org en
dc.subject Regression en
dc.subject Feature selection en
dc.subject Motor assessment en
dc.subject Rehabilitation en
dc.subject Wearable en
dc.title LASSO regression for monitoring patients progress following ACL reconstruction via motion sensors: a case study en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Salvatore Tedesco, Tyndall Micronano Electronics, University College Cork, Cork, Ireland. +353-21-490-3000 Email: salvatore.tedesco@tyndall.ie en
dc.internal.availability Full text available en
dc.date.updated 2019-09-16T11:10:33Z
dc.description.version Published Version en
dc.internal.rssid 499775013
dc.contributor.funder Enterprise Ireland en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle International Journal on Advances in Life Sciences en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress salvatore.tedesco@tyndall.ie en
dc.internal.IRISemailaddress brendan.oflynn@tyndall.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/ en


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