Related Experiment Video
Updated: May 25, 2026
07:46
Gait Analysis of Age-dependent Motor Impairments in Mice with Neurodegeneration
Published on: June 18, 2018
Statistical analysis of gait maturation in children based on probability density functions
Yunfeng Wu1, Zhangting Zhong, Meng Lu
1Department of Communication Engineering, School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China. y.wu@ieee.org
Summary
This study analyzed children's gait patterns using probability density functions (PDFs) to understand locomotor control maturation. Key findings show age-related differences in stride intervals, highlighting the impact of musculoskeletal growth on gait development.
Area of Science:
- Pediatrics
- Biomechanical Analysis
- Developmental Neuroscience
Background:
- Gait pattern analysis is crucial for understanding the maturation of locomotor control in children.
- Previous research has established the importance of temporal parameters in gait analysis.
Purpose of the Study:
- To estimate probability density functions (PDFs) of stride intervals in children using the Parzen-window method.
- To compute statistical gait measures and analyze gait maturation across different age groups.
Main Methods:
- Utilized the Parzen-window method to estimate PDFs of stride intervals for 50 children.
- Calculated averaged stride interval (ASI), variation of stride interval (VSI), PDF skewness (SK), and PDF kurtosis (KU).
- Compared gait maturation across three age groups: 3-5 years, 6-8 years, and 10-14 years.
Main Results:
- Significant differences in ASI and VSI were observed between the age groups.
- VSI decreased rapidly until age 8, then slowed.
- Positive SK values indicated slight stride interval distribution imbalance; age-dependent KU decrease suggested musculoskeletal growth effects.
Conclusions:
- Gait maturation in children is characterized by significant age-related changes in stride interval variability.
- The findings suggest that musculoskeletal development influences gait maturation.
- The study provides quantitative insights into the developmental trajectory of gait control.
More Related Videos
Related Concept Videos
01:22Applications of Normal Distribution
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
01:27Applications of Integration to Probability Density Functions
Continuous probability distributions are used to model random variables that can take on any real value within a specified range. These variables do not take on isolated or countable values but rather exist on a continuum. For example, the height of an individual can be measured with increasing precision—such as 163.5 or 165.25 centimeters—demonstrating that height is a continuous random variable.The behavior of such variables is described using a probability density function (PDF), which...
01:17Probability Histograms
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
01:32Probability Distributions
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
01:17z Scores and Area Under the Curve
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
02:32Variation: Normal Distribution, Range, and Standard Deviation
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...