Motion sensors-based machine learning approach for the identification of anterior cruciate ligament gait patterns in on-the-field activities in rugby players

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dc.contributor.authorTedesco, Salvatore
dc.contributor.authorCrowe, Colum
dc.contributor.authorRyan, Andrew
dc.contributor.authorSica, Marco
dc.contributor.authorScheurer, Sebastian
dc.contributor.authorClifford, Amanda M.
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2020-05-27T09:09:38Z
dc.date.available2020-05-27T09:09:38Z
dc.date.issued2020-05-27
dc.description.abstractAnterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.en
dc.description.sponsorshipEnterprise Ireland (Project SKYRE: Grant number CF-2015-0031-P); Science Foundation Ireland and European Regional Development Fund (Grant number 12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid3029en
dc.identifier.citationTedesco, S., Crowe, C., Ryan, A., Sica, M., Scheurer, S., Clifford, A. M., Brown, K. N., O’Flynn, B. (2020) ‘Motion sensors-based machine learning approach for the identification of anterior cruciate ligament gait patterns in on-the-field activities in rugby players’, Sensors, 20, 3029 (17pp). doi: 10.3390/s20113029en
dc.identifier.doi10.3390/s20113029en
dc.identifier.eissn1424-8220
dc.identifier.endpage17en
dc.identifier.issn1424-8220
dc.identifier.journaltitleSensorsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/10063
dc.identifier.volume20en
dc.language.isoenen
dc.publisherMDPIen
dc.rights© 2020, 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/).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMachine learningen
dc.subjectACLen
dc.subjectBiomechanicsen
dc.subjectIMUsen
dc.subjectInertial sensorsen
dc.subjectGait analysisen
dc.subjectRunningen
dc.subjectOn-the-fielden
dc.subjectRugbyen
dc.titleMotion sensors-based machine learning approach for the identification of anterior cruciate ligament gait patterns in on-the-field activities in rugby playersen
dc.typeArticle (peer-reviewed)en
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