Statistical Analysis of Gait Maturation in Children Using Nonparametric Probability Density Function Modeling

Analysis of gait dynamics in children may help understand the development of neuromuscular control and maturation of locomotor function. This paper applied the nonparametric Parzen-window estimation method to establish the probability density function (PDF) models for the stride interval time series of 50 children (25 boys and 25 girls). Four statistical parameters, in terms of averaged stride interval (ASI), variation of stride interval (VSI), PDF skewness (SK), and PDF kurtosis (KU), were computed with the Parzen-window PDFs to study the maturation of stride interval in children. By analyzing the results of the children in three age groups (aged 3–5 years, 6–8 years, and 10–14 years), we summarize the key findings of the present study as follows. (1) The gait cycle duration, in terms of ASI, increases until 14 years of age. On the other hand, the gait variability, in terms of VSI, decreases rapidly until 8 years of age, and then continues to decrease at a slower rate. (2) The SK values of both the histograms and Parzen-window PDFs for all of the three age groups are positive, which indicates an imbalance in the stride interval distribution within an age group. However, such an imbalance would be meliorated when the children grow up. (3) The KU values of both the histograms and Parzen-window PDFs decrease with the body growth in children, which suggests that the musculoskeletal growth enables the children to modulate a gait cadence with ease. (4) The SK and KU results also demonstrate the superiority of the Parzen-window PDF estimation method to the Gaussian distribution modeling, for the study of gait maturation in children.This work was supported by the Fundamental Research Funds for the Central Universities of China (grant no. 2010121061), the Natural Science Foundation of Fujian (grant no. 2011J01371), and the National Natural Science Foundation of China (grant no. 81101115). N. Xiang was supported by Xiamen University Undergraduate Innovation Training Project (grant no. XDDC201210384072). Z. T. Zhong was supported by the Fundamental Research Funds for the Central Universities (grant no. CXB2011023). J. He was supported by the Xiamen University undergraduate student innovative experiment project (grant no. XDDC2011007). The authors acknowledge Hausdorff et al. for providing the data of gait experiments with public access via PhysioNet