Titre :

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

Auteur(s) :

Thiry, Paul [Auteur]
Houry, Martin [Auteur]
Philippe, Laurent [Auteur]
Nocent, Olivier [Auteur]
Buisseret, Fabien [Auteur]
Dierick, Frédéric [Auteur]
Slama, Rim [Auteur]
Bertucci, William [Auteur]
Thévenon, André [Auteur]
444281|||Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369 - ULR 4488 [URePSSS]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Simoneau-Buessinger, Emilie [Auteur]

Titre de la revue :

Sensors

Nom court de la revue :

Sensors

Numéro :

22

Pagination :

5027

Éditeur :

MDPI AG

Date de publication :

2022-07-03

ISSN :

1424-8220

Mot(s)-clé(s) en anglais :

artificial intelligence
machine learning
inertial measurement unit—IMU
movement complexity
sample entropy
trunk flexion

Discipline(s) HAL :

Sciences du Vivant [q-bio]
Informatique [cs]/Interface homme-machine [cs.HC]
Informatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet Européen :

European Regional Development Fund (FEDER)

Établissement(s) :

Université de Lille
Univ. Artois
Univ. Littoral Côte d’Opale

Collections :

• Unité de Recherche Pluridisciplinaire Sport, Santé, Société (URePSSS) - ULR 7369

Équipe(s) de recherche :

Activité Physique, Muscle, Santé (APMS)

Date de dépôt :

2022-07-10T05:02:26Z
2022-07-11T09:16:28Z