LAMIH, CNRS, UMR 8201, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France.
CHU Lille, Université de Lille, 59000 Lille, France.
Sensors (Basel). 2022 Jul 3;22(13):5027. doi: 10.3390/s22135027.
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.
如今，更好地评估下腰痛（LBP）是一个重要的挑战，因为它是全球范围内导致残疾年数最多的肌肉骨骼疾病。本研究的目的是评估各种机器学习（ML）算法和样本熵（SampEn）的相关性，SampEn 评估运动变异性的复杂性，以识别下腰痛的状况。20 名慢性下腰痛（CLBP）患者和 20 名健康非 LBP 参与者进行了 1 分钟重复弯曲（屈曲）和返回（伸展）躯干运动。分析使用附在参与者身上的三个惯性传感器记录的时间序列进行。结果发现，CLBP 患者的 SampEn 明显较低，表明由于 LBP 导致运动复杂性丧失。高斯朴素贝叶斯 ML 被证明是各种测试算法中最好的，在识别 CLBP 患者方面达到了 79%的准确率。屈曲运动角速度是 ML 分析中最具判别力的特征。这项研究表明：监督机器学习和对躯干运动变异性的复杂性评估可用于识别 CLBP 状况，并且简单的运动学指标对此状况敏感。因此，ML 可以逐步被临床医生用于评估 CLBP 患者。
