Archibeck Erin, Halvorson Ryan, Silvestros Pavlos, Torres-Espin Abel, O'Connell Grace, Bailey Jeannie
Department of Mechanical Engineering, University of California, Berkeley, USA; Department of Orthopedic Surgery, University of California, San Francisco, USA.
Department of Orthopedic Surgery, University of California, San Francisco, USA.
J Biomech. 2025 Jun;186:112725. doi: 10.1016/j.jbiomech.2025.112725. Epub 2025 Apr 26.
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.
生物力学分析对于理解和监测肌肉骨骼损伤至关重要，对临床诊断和研究具有重要意义。当前的临床方法提供的是孤立的关节测量或定性观察，无法捕捉运动的复杂性。虽然三维生物力学测试较为全面，但其应用受到数据量的阻碍，难以得出具有临床相关性的结论。提取运动特征的方法通常会忽略时间序列数据，或者依赖于样本量。为了填补这些空白，本研究引入了运动复合评分（K评分），这是一种在保留个体变异性的同时提取高维运动特征的指标。本研究的目的是概述K评分算法的方法，突出其优点、局限性和应用。我们对K评分算法与（1）传统的孤立运动学测量方法，以及（2）传统主成分分析进行了比较研究。分析对象为一组慢性下腰痛（LBP）患者，他们表现出极大的运动异质性。在区分LBP患者与健康对照的整体运动方面，K评分优于传统的孤立指标（K评分：对照组=94.16±2.64，LBP组=85.82±7.73，p<0.001）。K评分在男性和女性参与者的整体运动方面也显示出显著差异，患有LBP的女性得分高于男性（p<0.001）。重要的是，K评分对体重指数（p=0.49）、年龄（p=0.14）、身高（p=0.11）或样本量不敏感。总之，K评分通过将全身时间序列数据封装在一个单一评分中，解决了传统方法的关键局限性，该评分可在不同的运动捕捉系统和活动中应用，使其成为临床生物力学研究的有力工具。
