Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms
Joint Authors
Wu, Meihong
Liao, Lifang
Ye, Xiaoquan
Yao, Yuchen
Chen, Pinnan
Shi, Lei
Huang, Hui
Luo, Xin
Wu, Yunfeng
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence.
In this paper, we computed the sample entropy (SampEn) and average stride interval (ASI) parameters to quantify the stride series of 50 gender-matched children participants in three age groups.
We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively.
Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test ( p < 0.01 ) in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth.
The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years), middle (aged 6–8 years), and elder (aged 10–14 years) children.
Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems.
In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns.
These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%), recall (≥0.8), and precision (≥0.8077).
American Psychological Association (APA)
Wu, Meihong& Liao, Lifang& Luo, Xin& Ye, Xiaoquan& Yao, Yuchen& Chen, Pinnan…[et al.]. 2016. Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms. BioMed Research International،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099261
Modern Language Association (MLA)
Wu, Meihong…[et al.]. Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms. BioMed Research International No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1099261
American Medical Association (AMA)
Wu, Meihong& Liao, Lifang& Luo, Xin& Ye, Xiaoquan& Yao, Yuchen& Chen, Pinnan…[et al.]. Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099261
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099261