Ardestani Marzieh M, Moazen Mehran, Jin Zhongmin
State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi'an JiaoTong University, 710049 Xi'an, Shaanxi, China.
Medical and Biological Engineering, School of Engineering, University of Hull, Hull, UK.
Med Eng Phys. 2015 Feb;37(2):165-74. doi: 10.1016/j.medengphy.2014.11.012. Epub 2014 Dec 29.
Despite the widespread applications of human gait analysis, causal interactions between joint kinematics and joint moments have not been well documented. Typical gait studies are often limited to pure multi-body dynamics analysis of a few subjects which do not reveal the relative contributions of joint kinematics to joint moments. This study presented a computational approach to evaluate the sensitivity of joint moments due to variations of joint kinematics. A large data set of probabilistic joint kinematics and associated ground reaction forces were generated based on experimental data from literature. Multi-body dynamics analysis was then used to calculate joint moments with respect to the probabilistic gait cycles. Employing the principal component analysis (PCA), the relative contributions of individual joint kinematics to joint moments were computed in terms of sensitivity indices (SI). Results highlighted high sensitivity of (1) hip abduction moment due to changes in pelvis rotation (SI = 0.38) and hip abduction (SI = 0.4), (2) hip flexion moment due to changes in hip flexion (SI = 0.35) and knee flexion (SI = 0.26), (3) hip rotation moment due to changes in pelvis obliquity (SI = 0.28) and hip rotation (SI = 0.4), (4) knee adduction moment due to changes in pelvis rotation (SI = 0.35), hip abduction (SI = 0.32) and knee flexion (SI = 0.34), (5) knee flexion moment due to changes in pelvis rotation (SI = 0.29), hip flexion (SI = 0.28) and knee flexion (SI = 0.31), and (6) knee rotation moment due to changes in hip abduction (SI = 0.32), hip flexion and knee flexion (SI = 0.31). Highlighting the "cause-and-effect" relationships between joint kinematics and the resultant joint moments provides a fundamental understanding of human gait and can lead to design and optimization of current gait rehabilitation treatments.
尽管人体步态分析有着广泛的应用，但关节运动学与关节力矩之间的因果相互作用尚未得到充分记录。典型的步态研究通常局限于对少数受试者进行纯粹的多体动力学分析，而这并不能揭示关节运动学对关节力矩的相对贡献。本研究提出了一种计算方法，用于评估由于关节运动学变化而导致的关节力矩敏感性。基于文献中的实验数据，生成了一个包含概率性关节运动学和相关地面反作用力的大数据集。然后，利用多体动力学分析来计算相对于概率性步态周期的关节力矩。采用主成分分析（PCA），根据敏感性指数（SI）计算了各个关节运动学对关节力矩的相对贡献。结果突出显示了以下方面的高敏感性：（1）由于骨盆旋转变化（SI = 0.38）和髋关节外展（SI = 0.4）导致的髋关节外展力矩；（2）由于髋关节屈曲变化（SI = 0.35）和膝关节屈曲变化（SI = 0.26）导致的髋关节屈曲力矩；（3）由于骨盆倾斜变化（SI = 0.28）和髋关节旋转变化（SI = 0.4）导致的髋关节旋转力矩；（4）由于骨盆旋转变化（SI = 0.35）、髋关节外展变化（SI = 0.32）和膝关节屈曲变化（SI = 0.34）导致的膝关节内收力矩；（5）由于骨盆旋转变化（SI = 0.29）、髋关节屈曲变化（SI = 0.28）和膝关节屈曲变化（SI = 0.31）导致的膝关节屈曲力矩；以及（6）由于髋关节外展变化（SI = 0.32）、髋关节屈曲和膝关节屈曲变化（SI = 0.31）导致的膝关节旋转力矩。突出关节运动学与合成关节力矩之间的“因果”关系，有助于从根本上理解人体步态，并可为当前步态康复治疗的设计和优化提供指导。
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