Point-of-care motion capture and biomechanical assessment improve clinical utility of dynamic balance testing for lower extremity osteoarthritis

Abstract

Musculoskeletal conditions impede patient biomechanical function. However, clinicians rely on subjective functional assessments with poor test characteristics for biomechanical outcomes because more advanced assessments are impractical in the ambulatory care setting. Using markerless motion capture (MMC) in clinic to record time-series joint position data, we implemented a spatiotemporal assessment of patient kinematics during lower extremity functional testing to evaluate whether kinematic models could identify disease states beyond conventional clinical scoring. 213 trials of the star excursion balance test (SEBT) were recorded by 36 subjects during routine ambulatory clinic visits using both MMC technology and conventional clinician scoring. Conventional clinical scoring failed to distinguish patients with symptomatic lower extremity osteoarthritis (OA) from healthy controls in each component of the assessment. However, principal component analysis of shape models generated from MMC recordings revealed significant differences in subject posture between the OA and control cohorts for six of the eight components. Additionally, time-series models of subject posture change over time revealed distinct movement patterns and reduced overall postural change in the OA cohort compared to the controls. Finally, a novel metric quantifying postural control was derived from subject specific kinematic models and was shown to distinguish OA (1.69), asymptomatic postoperative (1.27), and control (1.23) cohorts (p = 0.0025) and to correlate with patient-reported OA symptom severity (R = -0.72, p = 0.018). Time series motion data have superior discriminative validity and clinical utility than conventional functional assessments in the case of the SEBT. Novel spatiotemporal assessment approaches can enable routine in-clinic collection of objective patient-specific biomechanical data for clinical decision-making and monitoring recovery.

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“…Data for the subsequent four trials was temporally normalized using min-max scaling to ensure uniform data points across all patients. Inspired by previous work in our lab 34 , Principal Component Analysis (PCA) was performed using Python scikit-learn library 39 to quantify movement control for each patient's data, by encompassing landmark positions for every normalized time point. Then, Generalized Procrustes Analysis 40 was applied to PCA-transformed data using the calculated alignment at resting position (t=0 for each STS repetition) to standardize data across patients, mitigating confounding factors due to anthropometric differences and PCA indeterminacies, such as rotation.…”

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