• Open Access
    • Comment cela fonctionne?
    • Ouvrir une session
    • Contact

    Voir le document

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Voir le document 
    • Accueil de LUCK
    • HE Louvain en Hainaut
    • CEREF
    • Sciences Et Techniques
    • Voir le document
    • Accueil de LUCK
    • HE Louvain en Hainaut
    • CEREF
    • Sciences Et Techniques
    • Voir le document
    Voir/Ouvrir
    sensors-22-05027.pdf (1.660Mo)
    Date
    2022-07-03
    Auteur
    Thiry, Paul
    Houry, Martin
    Philippe, Laurent
    Nocent, Olivier
    Buisseret, Fabien
    Dierick, Frédéric
    Slama, Rim
    Bertucci, William
    Thévenon, André
    Simoneau, Emilie
    Metadata
    Afficher la notice complète

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

    Résumé
    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.

    Parcourir

    Tout LUCKCommunautés & CollectionsAuteurTitreDate de publicationSujetType de documentTitre de périodiqueThématiqueCette collectionAuteurTitreDate de publicationSujetType de documentTitre de périodiqueThématique

    Mon compte

    Ouvrir une sessionS'inscrire

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Plan du site

    • Open Access
    • Comment cela fonctionne?
    • Mon compte

    Contact

    • L’équipe de LUCK
    • Synhera
    • CIC