Abstract
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.
Highlights
Human locomotion functions are regulated by the central nervous system, and coordinated by the musculoskeletal system
Statistical analysis of gait dynamics based on probability density function (PDF) can provide more accurate quantitative parameters of gait that may affect the analytic inference or medical decision
PDF closer to the distribution in nature, such that the probability density function modeling supports the measurement of temporal-spatial gait parameters with a higher degree of accuracy
Summary
Human locomotion functions are regulated by the central nervous system, and coordinated by the musculoskeletal system. The immature motor control of young children usually results in some unstable walking patterns and erratic posture [1]. An infant is able to sit upright at about 6 months after birth, begins to crawl after 9 months, and walks with immature control of posture at around months [2]. When young children first learn to walk, immature motor control leads to large fluctuations from one stride interval (time from initial contact of one foot to the subsequent contact of the same foot) to another [1]. According to Sutherland [6], the gait in young children about 4 years old will become relatively mature, with a more stable walking pattern. Norlin et al [7] studied 230 individuals from 3 to 16 years old, and reported that the gait had not become mature until 8 years old
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