Abstract:

Most of the recent studies focus on human walking and running movements that we often use in daily life and many athletics events. The aim of the study is to reduce the dimension of kinematics data by using the Principal Component Analysis method and describing human movement at different speeds by low-dimensional Fourier model. In order to collect kinematics data of running movement a short distance runner (age:26, height:1.82m, kg:76kg) was asked to run on treadmill at 8km/h, 12km/h and 16km/h running speed and 6 strides were captured. Principal Components of data including instantaneous positions which were described 3D position values of 16 anatomical markers attached to the subject. It was observed that the first four Main Components can cover over 98% of original data and Running at different velocities can be effectively defined by using low-dimensional Fourier series. It was observed that the original spatial locations of the anatomical points which constitute the positions in each instant are consistent with the locations derived from the constructed running model (8km/h, R=0.97, 12km/h, R=0.94 and 16km/h, R=0.93). In this study, it has been determined that human running at varying speeds can be defined with lower dimensional data by modeling the behaviors of the first 4 components derived by using PCA method. Although components derived from PCA do not correspond to a parameter in reality, it can be seen that the second component represents the movement of the feet, the third component represents the movement of the arms and the fourth component represents bouncing structure in the running process. The PCs identified in the data belonging to larger amounts of individuals and various positions, can make it possible to classify, analyze, diagnose, compare and collate between movement positions depending on different situations such as gender, running speed, fatigue, physical structure, injury and well arrangement of technique.

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