Machine Learning Identifies Chronic Low ...
Document type :
Article dans une revue scientifique: Article original
DOI :
PMID :
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Title :
Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test.
Author(s) :
Thiry, Paul [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Houry, Martin [Auteur]
Philippe, Laurent [Auteur]
Nocent, Olivier [Auteur]
Performance, Santé, Métrologie, Société - EA 7507 [PSMS]
Buisseret, Fabien [Auteur]
Université de Mons / University of Mons [UMONS]
Dierick, Frédéric [Auteur]
Université de Mons / University of Mons [UMONS]
Slama, Rim [Auteur]
Laboratoire d'Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires [LINEACT]
Bertucci, William [Auteur]
Performance, Santé, Métrologie, Société - EA 7507 [PSMS]
Thevenon, André [Auteur]
Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369 - ULR 4488 [URePSSS]
Simoneau-Buessinger, Emilie [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Houry, Martin [Auteur]
Philippe, Laurent [Auteur]
Nocent, Olivier [Auteur]
Performance, Santé, Métrologie, Société - EA 7507 [PSMS]
Buisseret, Fabien [Auteur]
Université de Mons / University of Mons [UMONS]
Dierick, Frédéric [Auteur]
Université de Mons / University of Mons [UMONS]
Slama, Rim [Auteur]
Laboratoire d'Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires [LINEACT]
Bertucci, William [Auteur]
Performance, Santé, Métrologie, Société - EA 7507 [PSMS]
Thevenon, André [Auteur]

Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369 - ULR 4488 [URePSSS]
Simoneau-Buessinger, Emilie [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Journal title :
Sensors
Abbreviated title :
Sensors
Volume number :
22
Publisher :
MDPI
Publication date :
2022-07-03
ISSN :
1424-8220
English keyword(s) :
Bayes Theorem
Biomechanical Phenomena
Humans
Low Back Pain
Machine Learning
Movement
Torso
artificial intelligence
inertial measurement unit—IMU
machine learning
movement complexity
sample entropy
trunk flexion
Biomechanical Phenomena
Humans
Low Back Pain
Machine Learning
Movement
Torso
artificial intelligence
inertial measurement unit—IMU
machine learning
movement complexity
sample entropy
trunk flexion
HAL domain(s) :
Sciences du Vivant [q-bio]
English abstract : [en]
Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the ...
Show more >Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients.Show less >
Show more >Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
Univ. Artois
Univ. Littoral Côte d’Opale
Univ. Artois
Univ. Littoral Côte d’Opale
Research team(s) :
Activité Physique, Muscle, Santé (APMS)
Submission date :
2024-02-08T06:27:44Z
2024-02-14T09:16:58Z
2024-02-14T09:16:58Z
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