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Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test

dc.rights.licenseCC0en_US
dc.contributor.authorBUISSERET, Fabien
dc.contributor.authorHOURY, Martin
dc.contributor.authorHage, R.
dc.contributor.authorDIERICK, Frédéric
dc.date.accessioned2022-04-21T19:22:00Z
dc.date.available2022-04-21T19:22:00Z
dc.date.issued2022
dc.identifier.urihttps://luck.synhera.be/handle/123456789/1613
dc.identifier.doihttps://doi.org/10.3390/s22072805en_US
dc.description.abstractUnderstanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.en_US
dc.description.sponsorshipEURen_US
dc.language.isoENen_US
dc.publisherSensorsen_US
dc.relation.isreferencedbyhttps://www.mdpi.com/1424-8220/22/7/2805en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.fren_US
dc.subjectNeck painen_US
dc.subjectMachine learningen_US
dc.subjectHead rotationen_US
dc.titleHead Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Testen_US
dc.typeArticle scientifiqueen_US
synhera.classificationIngénierie, informatique & technologie>>Ingénierie électrique & électroniqueen_US
synhera.institutionHENALLUXen_US
synhera.institutionCeREF Techniqueen_US
synhera.otherinstitutionUMONSen_US
synhera.otherinstitutionUCLouvainen_US
synhera.otherinstitutionRehazenteren_US
synhera.stakeholders.fundInterreg FWVl NOMADeen_US
synhera.cost.total2359en_US
synhera.cost.apc2359en_US
synhera.cost.comp0en_US
synhera.cost.acccomp0en_US
dc.description.versionOuien_US
dc.rights.holderCEREFen_US


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