Biomechanical analysis is essential for understanding and monitoring musculoskeletal impairments, with implications for clinical diagnostics and research. Current clinical methods provide isolated joint measures or qualitative observations, failing to capture motion complexity. While 3D biomechanical testing is comprehensive, its application is hindered by data volume, making it challenging to derive clinically relevant conclusions. Approaches to distill motion often neglect time-series data or are dependent on population size. To address these gaps, this study introduces the Kinematic Composite Score (K-Score), a metric that distills high-dimensional motion while preserving individual variability.

The objective of this research is to outline the methodology of the K-Score algorithm, highlight its strengths, limitations, and applications. We conducted a comparative study of the K-Score Algorithm against (1) the conventional isolated kinematic measures, and (2) traditional Principal Component Analysis. The analysis was conducted with a cohort of chronic low back pain (LBP) patients, who exhibit tremendous movement heterogeneity.

The K-Score outperformed traditional isolated metrics in differentiating overall motion of LBP patients from healthy controls (K-Score:​ controls = 94.16 ± 2.64, LBP = 85.82 ± 7.73, p < 0.001). The K-Score also demonstrated significant differences in overall motion between male and female participants, where females with LBP demonstrated higher scores than males (p < 0.001). Importantly, the K-Score was not sensitive to BMI (p = 0.49), age (p = 0.14), height (p = 0.11), or sample size. In conclusion, the K-Score addresses key limitations of traditional approaches by encapsulating full-body, time-series data within a single score that is adaptable across motion capture systems and activities, making it a powerful tool for clinical biomechanics research.