Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain

Overview

Journal Front Bioeng Biotechnol

Date 2022 May 2

PMID 35497350

Authors

Anastasia V Keller

Abel Torres-Espin

Thomas A Peterson

Jacqueline Booker

Conor ONeill

Jeffrey C Lotz

Jeannie F Bailey

Adam R Ferguson

Robert P Matthew

Affiliations

Soon will be listed here.

Abstract

Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology.

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