Compensatory biomechanics and spinal loading during dynamic maneuvers in patients with chronic low back pain.

PURPOSE: This study explores the biomechanics underlying the sit-to-stand (STS) functional maneuver in chronic LBP patients to understand how different spinal disorders and levels of pain severity relate to unique compensatory biomechanical behaviors. This work stands to further our understanding of the relationship between spinal loading and symptoms in LBP patients. METHODS: We collected in-clinic motion data from 44 non-specific LBP (NS-LBP) and 42 spinal deformity LBP (SD-LBP) patients during routine clinical visits. An RGB-depth camera tracked 3D joint positions from the frontal view during unassisted, repeated STS maneuvers. Patient-reported outcomes (PROs) for back pain (VAS) and low back disability (ODI) were collected during the same clinical visit. RESULTS: Between patient groups, SD-LBP patients had 14.3% greater dynamic sagittal vertical alignment (dSVA) and 10.1% greater peak spine torque compared to NS-LBP patients (p < 0.001). SD-LBP patients also had 11.8% greater hip torque (p < 0.001) and 86.7% greater knee torque (p = 0.04) compared to NS-LBP patients. There were no significant differences between patient groups in regard to anterior or vertical torso velocities, but anterior and vertical torso velocities correlated with both VAS (r = - 0.38, p < 0.001) and ODI (r = - 0.29, p = 0.01). PROs did not correlate with other variables. CONCLUSION: Patients with LBP differ in movement biomechanics during an STS transfer as severity of symptoms may relate to different compensatory strategies that affect spinal loading. Further research aims to establish relationships between movement and PROs and to inform targeted rehabilitation approaches.

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

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.

Automated assessment and classification of spine, hip, and knee pathologies from sit-to-stand movements collected in clinical practice.

Efficient, cost-effective methods for quantifying patient biomechanics at the point of care can facilitate faster and more accurate diagnoses. This work presents a new method to diagnose pre-surgical back, hip, and knee patients by analysing their sit-to-stand motion captured by a Kinect camera. Kinematic and dynamic time-series features were extracted from patient movements collected in clinic. These features were used to test a variety of machine learning methods for patient classification. The performance of models trained on time-series features were compared against models trained on domain-knowledge features, highlighting the importance of using time-series data for the classification of human movement. Additionally, the effectiveness of using semi-supervised learning is tested on partially labelled datasets, providing insight on how to boost classification performance in situations where labelled patient data is difficult to obtain. The best semi-supervised model achieves ∼73% accuracy in distinguishing individuals with low-back pain, and hip and knee degeneration from control subjects.