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

Abstract

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.

Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning

“…However, our study diverges by offering a broader analysis with three different functional exercises (flex, tot, and lat. ), expanding the scope beyond our singular exercise focus [35]. Our study builds upon the work of De la Torre et al, whose research involves a performance comparison of classification algorithms for MoCap triaxial movement exercises, while their emphasis is on cervical assessment, our study focuses specifically on the lumbar region.…”

Wearable Sensors Applied in Movement Analysis

“…The study was conducted with 38 acute and subacute non-specific neck pain patients and 42 healthy control participants and demonstrates that machine-learning methods can provide relevant information from relatively small datasets. The same observation is made in [ 10 ], where the kinematics of 20 patients with chronic low-back pain (CLBP) and 20 healthy participants without CLBP were recorded from three IMUs attached to the participants while they performed 1-min repetitive bending (flexion) and return (extension) trunk movements. It was found that Gaussian Naive Bayes machine learning achieved 79% accuracy in identifying CLBP patients.…”

Novel Ensemble Learning Algorithm for Early Detection of Lower Back Pain Using Spinal Anomalies

“…In a previous study [16], the proposed GNB machine learning model predicted the risk of chronic LBP. General characteristics, including sex, age, BMI, and physical activity level, were collected from all participants using the Global Physical Activity Questionnaire.…”