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Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

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Interpretable machine learning models for classifying low back pain status using functional physiological variables.

Bernard X W Liew¹, David Rugamer^2,3, Alessandro Marco De Nunzio⁴

¹School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, CO4 3SQ, Essex, UK. bl19622@essex.ac.uk.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

|March 4, 2020

Summary

This summary is machine-generated.

Statistical models accurately distinguished low back pain (LBP) subtypes from healthy controls using electromyography (EMG) and kinematic data during lifting. These models can identify movement impairments linked to LBP risk.

Keywords:

Biomechanics Functional regression Lifting Low back pain Machine learning Motor control

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Area of Science:

Biomechanics
Rehabilitation Science
Data Science in Healthcare

Background:

Low back pain (LBP) is a prevalent condition with diverse subtypes.
Understanding the physiological differences between LBP subtypes and healthy individuals is crucial for targeted interventions.
Electromyography (EMG) and kinematic data offer insights into movement patterns during functional tasks.

Purpose of the Study:

To assess the predictive accuracy of statistical models in differentiating LBP subtypes from healthy controls.
To utilize time-varying electromyographic and kinematic signals as predictors for LBP classification.
To identify key muscle activation and movement patterns associated with different LBP statuses.

Main Methods:

Collected motion capture and EMG data from 49 participants (16 healthy controls, 16 remission LBP, 17 current LBP) during a low-load lifting task.
Developed three statistical models using functional data boosting (FDboost) for binary classification (control vs. LBP, control vs. remission LBP, remission LBP vs. LBP).
Utilized 40 kinematic and EMG variables, with 31-32 predictors included after addressing collinearity and incorporating sex as a covariate.

Main Results:

Model 1 (control vs. LBP) achieved an Area Under the Curve (AUC) of 90.4% with 7 EMG predictors, highlighting biceps femoris.
Model 2 (control vs. remission LBP) achieved an AUC of 91.2% with 9 predictors, with deltoid as most influential.
Model 3 (remission LBP vs. LBP) achieved a high AUC of 96.7% with 7 predictors, identifying iliocostalis as the key predictor.

Conclusions:

Statistical models effectively predict LBP subtypes using EMG and kinematic data during lifting.
The identified influential muscles (biceps femoris, deltoid, iliocostalis) represent key movement impairments.
Translating physiological differences into clinical insights can guide future prognostic research for LBP.