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

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 f

SUBMITTER: Keller AV 

PROVIDER: S-EPMC9047543 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature