Deschamps Kevin, Eerdekens Maarten, Geentjens Jurre, Santermans Lieselot, Steurs Lien, Dingenen Bart, Thysen Maarten, Staes Filip
KU Leuven, Department of Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Group, Belgium; KU Leuven, Laboratory for Clinical Motion Analysis, University Hospital Pellenberg, Belgium; Parnasse-ISEI, Department of Podiatry, Avenue E. Mounier, 84-1200, Bruxelles, Belgium; Artevelde University College Ghent, Department of Podiatry, Ghent, Belgium.
KU Leuven, Laboratory for Clinical Motion Analysis, University Hospital Pellenberg, Belgium.
Gait Posture. 2018 May;62:372-377. doi: 10.1016/j.gaitpost.2018.03.051. Epub 2018 Mar 31.
A comprehensive perspective on foot and lower limb joint coupling is lacking since previous studies did not consider the multi-articular nature of the foot and lower limb neither accounted for biomechanical heterogeneity.
The current manuscript describes a novel methodological process for detection and exploration of joint coupling patterns in the lower limb kinetic chain.
The first stage of the methodological process encompasses the measurement of 3D joint kinematics of the foot and lower limb kinetic chain during dynamic activities. The second stage consists of selecting the kinematic waveforms of interest. In the third stage, cross-correlation coefficients are calculated across the selected one-dimensional continua of each subject. In the fourth stage, all cross-correlation coefficients per subject are used as input variable in a cluster algorithm. Algorithm specific qualitative metrics are subsequently considered to determine the most robust clustering. Finally, in the fifth stage the process of biomechanical interpretation is initiated and further exploration is recommended by triangulating with other biomechanical variables.
A first clinical illustration of the novel method was provided using data of fourteen young elite athletes. Cross-correlation coefficients for each leg were calculated across continua of the pelvis, hip, knee, rear foot and midfoot. A hierarchical clustering approach stratified the coefficients into two distinct clusters which was mainly guided by the frontal plane knee kinematics. Both clustered differed significantly from each other with respect to their frontal plane ankle, knee and hip kinetics.
The presented method seems to provide a valuable approach to gain insight into foot and lower joint coupling.
由于先前的研究没有考虑到足部和下肢的多关节性质，也没有考虑生物力学异质性，因此缺乏对足部和下肢关节耦合的全面认识。
当前的手稿描述了一种用于检测和探索下肢动力链中关节耦合模式的新方法过程。
该方法过程的第一阶段包括在动态活动期间测量足部和下肢动力链的三维关节运动学。第二阶段包括选择感兴趣的运动学波形。在第三阶段，计算每个受试者所选一维连续体的互相关系数。在第四阶段，将每个受试者的所有互相关系数用作聚类算法的输入变量。随后考虑算法特定的定性指标以确定最稳健的聚类。最后，在第五阶段启动生物力学解释过程，并建议通过与其他生物力学变量进行三角测量来进行进一步探索。
使用14名年轻精英运动员的数据提供了该新方法的首个临床例证。计算了每条腿在骨盆、髋部、膝盖、后足和中足连续体上的互相关系数。层次聚类方法将系数分为两个不同的聚类，这主要由额面膝盖运动学指导。两个聚类在额面踝关节、膝盖和髋部动力学方面彼此有显著差异。
所提出的方法似乎为深入了解足部和下肢关节耦合提供了一种有价值的方法。
