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    • Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return TestPeer reviewedOpen access 

      03 juillet 2022, Thiry, Paul; HOURY, Martin; PHILIPPE, Laurent; Nocent, Olivier; BUISSERET, Fabien; DIERICK, Frédéric; Slama, Rim; Bertucci, William; Thévenon, André; Simoneau, Emilie, CeREF Technique
      Article scientifique
      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. ...

    • NOMADe : présentation du projet et premières réalisationsPeer reviewedOpen access 

      11 décembre 2020, BUISSERET, Fabien; BONGE, Elinore; DEHOUCK, Stéphanie; EGGERMONT, Stéphanie; ESTIEVENART, Wesley; Gérard, Martine; Hage, Renaud; Thiry, Paul; DIERICK, Frédéric; VELINGS, Nicolas, CeREF Technique
      Article scientifique
      Où le projet INTERREG FWVL NOMADe (NeurOMuskuloskeletAl Disorders – e-learning ecosystem) est présenté, ainsi que deux réalisations illustrant la première année de son déroulement

    • Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain PatientsPeer reviewedOpen access 

      22 mars 2022, Thiry, Paul; Nocent, Olivier; BUISSERET, Fabien; Bertucci William; Thévenon, André; Simoneau-Buessinger, Emilie, CeREF Technique
      Article scientifique
      Low back pain (LBP) obviously reduces the quality of life but is also the world’s leading cause of years lived with disability. Alterations in motor response and changes in movement patterns are expected in LBP patients when compared to healthy people. Such changes in dynamics may be assessed by the nonlinear analysis of kinematical time series recorded from one patient’s motion. Since sample ...