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. Author summary Osteoarthritis (OA) is a leading cause of disability in the United States. Despite the relevance of biomechanical function as a marker of disease severity and as a target for therapeutic interventions, clinical assessments of biomechanical function are significantly limited by clinician subjectivity and poor test characteristics while more advanced methods are not feasible due to the need for specialized equipment and trained personnel. Coupling a single markerless motion capture camera with statistical modeling of posture change, we developed a practical system to perform advanced biomechanical assessments of lower extremity function during routine clinic visits. To validate our system, OA patients and healthy controls were assessed performing a functional balance task by clinicians according to conventional scoring and separately by our motion capture system using kinematic posture modeling. Although clinical scoring failed to distinguish OA patients and healthy controls, our kinematic modeling and dimensionality reduction techniques identified significant differences in both subject posture and motion trajectories throughout the assessment. Furthermore, OA patients reporting more severe symptoms exhibited worse postural control. Our results imply that novel motion capture approaches can enable routine in-clinic collection of objective patient-specific biomechanical data for clinical decision-making and monitoring recovery.