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

• DOI: 10.3389/fbioe.2022.868684
• Publication Date: 2022-04-14T12:48:55Z
• Authors: Anastasia V. Keller; Abel Torres-Espin; Thomas A. Peterson; Jacqueline Booker; Conor O’Neill; Jeffrey C Lotz; Jeannie F Bailey; Adam R. Ferguson; Robert P. Matthew
• Keywords: Clinical Sciences; Medical Biotechnology; Biomedical Engineering; Medical biotechnology; 610; Bioengineering; Industrial biotechnology; biomechanics; 03 medical and health sciences; 0302 clinical medicine; Clinical Research; 616; Machine Learning and Artificial Intelligence; nonlinear principal component analysis; Biomedical and Clinical Sciences; Pain Research; Rehabilitation; Neurosciences; Bioengineering and Biotechnology; 4.1 Discovery and preclinical testing of markers and technologies; 3. Good health; sit-to-stand; movement strategies; Physical Rehabilitation; Networking and Information Technology R&D (NITRD); Back Pain; chronic low back pain; Chronic Pain; Other Biological Sciences; Biomedical engineering; TP248.13-248.65; Biotechnology

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.