Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test
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...
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/13/5027 |
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| author | Paul Thiry Martin Houry Laurent Philippe Olivier Nocent Fabien Buisseret Frédéric Dierick Rim Slama William Bertucci André Thévenon Emilie Simoneau-Buessinger |
| author_facet | Paul Thiry Martin Houry Laurent Philippe Olivier Nocent Fabien Buisseret Frédéric Dierick Rim Slama William Bertucci André Thévenon Emilie Simoneau-Buessinger |
| author_sort | Paul Thiry |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:25:01Z |
| format | Article |
| id | doaj.art-299430a691734f4989fc05309ddce764 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:25:01Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-299430a691734f4989fc05309ddce7642023-12-01T21:42:18ZengMDPI AGSensors1424-82202022-07-012213502710.3390/s22135027Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return TestPaul Thiry0Martin Houry1Laurent Philippe2Olivier Nocent3Fabien Buisseret4Frédéric Dierick5Rim Slama6William Bertucci7André Thévenon8Emilie Simoneau-Buessinger9LAMIH, CNRS, UMR 8201, Université Polytechnique Hauts-de-France, 59313 Valenciennes, FranceCentre de Recherche FoRS, Haute-Ecole de Namur-Liège-Luxembourg (Henallux), Rue Victor Libert 36H, 6900 Marche-en-Famenne, BelgiumCentre de Recherche FoRS, Haute-Ecole de Namur-Liège-Luxembourg (Henallux), Rue Victor Libert 36H, 6900 Marche-en-Famenne, BelgiumPSMS, Université de Reims Champagne Ardenne, 51867 Reims, FranceCeREF Technique, Chaussée de Binche 159, 7000 Mons, BelgiumCeREF Technique, Chaussée de Binche 159, 7000 Mons, BelgiumLINEACT Laboratory, CESI Lyon, 69100 Villeurbanne, FrancePSMS, Université de Reims Champagne Ardenne, 51867 Reims, FranceCHU Lille, Université de Lille, 59000 Lille, FranceLAMIH, CNRS, UMR 8201, Université Polytechnique Hauts-de-France, 59313 Valenciennes, FranceNowadays, 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.https://www.mdpi.com/1424-8220/22/13/5027artificial intelligencemachine learninginertial measurement unit—IMUmovement complexitysample entropytrunk flexion |
| spellingShingle | Paul Thiry Martin Houry Laurent Philippe Olivier Nocent Fabien Buisseret Frédéric Dierick Rim Slama William Bertucci André Thévenon Emilie Simoneau-Buessinger Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test Sensors artificial intelligence machine learning inertial measurement unit—IMU movement complexity sample entropy trunk flexion |
| title | Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test |
| title_full | Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test |
| title_fullStr | Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test |
| title_full_unstemmed | Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test |
| title_short | Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test |
| title_sort | machine learning identifies chronic low back pain patients from an instrumented trunk bending and return test |
| topic | artificial intelligence machine learning inertial measurement unit—IMU movement complexity sample entropy trunk flexion |
| url | https://www.mdpi.com/1424-8220/22/13/5027 |
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