Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test
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CeREF Technique, Chaussée de Binche 159, 7000 Mons, Belgium
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Traitement Formation Thérapie Manuelle (TFTM), Private Physiotherapy/Manual Therapy Center, Avenue des Cerisiers 211A, 1200 Brussels, Belgium
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Faculté des Sciences de la Motricité, UCLouvain, Place Pierre de Coubertin 1, 1348 Ottignies-Louvain-la-Neuve, Belgium
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Service de Physique Nucléaire et Subnucléaire, UMONS, Research Institute for Complex Systems, Place du Parc 20, 7000 Mons, Belgium
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Centre de Recherche FoRS, Haute-Ecole de Namur-Liège-Luxembourg (Henallux), Rue Victor Libert 36H, 6900 Marche-en-Famenne, Belgium
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Laboratoire d’Analyse du Mouvement et de la Posture (LAMP), Centre National de Rééducation Fonctionnelle et de Réadaptation–Rehazenter, Rue André Vésale 1, 2674 Luxembourg, Luxembourg
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Ricciardi
Sensors 2022, 22(7), 2805; https://doi.org/10.3390/s22072805
Received: 14 March 2022
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Revised: 31 March 2022
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Accepted: 3 April 2022
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Published: 6 April 2022
(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)
Understanding 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.
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Keywords:
artificial intelligence; supervised machine learning; kinematics; head rotation test; neck pain
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MDPI and ACS Style
Hage, R.; Buisseret, F.; Houry, M.; Dierick, F. Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test. Sensors 2022, 22, 2805. https://doi.org/10.3390/s22072805
AMA Style
Hage R, Buisseret F, Houry M, Dierick F. Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test. Sensors. 2022; 22(7):2805. https://doi.org/10.3390/s22072805
Chicago/Turabian StyleHage, Renaud, Fabien Buisseret, Martin Houry, and Frédéric Dierick. 2022. "Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test" Sensors 22, no. 7: 2805. https://doi.org/10.3390/s22072805
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