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

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.

Extended student quality improvement programs of Xiamen University

“…There were 3 student projects supported by the Xiamen University student innovation research grants. Our students have published 7 peer-reviewed international journal papers [14]- [17], [20], [22], [24] and 7 conference papers [25]- [31]. …”

Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms

“…Because the gait speed and other phase parameters are often altered by the accelerating or decelerating movements when the subject starts or stops walking, it is necessary to eliminate the start-up or ending effects of walking posture in the gait data. In the present work, the data samples of the stride interval series recorded in the first 60 s and the last 5 s were removed, respectively, which was the same as implemented in the previous related studies [ 9 , 17 ]. The stride outliers whose amplitude values were larger or smaller than three times standard deviations of the median of each stride interval series were detected and removed by a median filter [ 17 , 20 ].…”

“…Sharma et al [ 15 ] extracted the Shannon entropy, Renyi entropy, approximate entropy, and SampEn features from the EEG signal components derived from the empirical mode decomposition algorithm and then employed the least-squares support vector machine to discriminate the focal EEG signals. In our previous studies [ 16 , 17 ], we applied the nonparametric statistical methods to establish the probability density models of stride series for the adolescents at different ages.…”

Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

“…The kernel density estimation can be used to approximate arbitrary multimodal distributions without the assumption of underlying density functions [22,23]. In the present study, we used the kernel functions to estimate the class-conditional probability density of the bivariate features for the two VAG signal groups respectively.…”