title: Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms.
abstract: 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).
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Meihong
authors.initials: M
authors.lastname: Wu
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Lifang
authors.initials: L
authors.lastname: Liao
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Luo
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Ye
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Yao
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Pinnan
authors.initials: P
authors.lastname: Chen
authors.affiliation: Department of Orthopedics, Zhongshan Hospital, Xiamen University, 201 Hubin South Road, Xiamen, Fujian 361004, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Rehabilitation, Zhongshan Hospital, Xiamen University, 201 Hubin South Road, Xiamen, Fujian 361004, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Huang
authors.affiliation: School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
authors.forename: Yunfeng
authors.identifier: 0000-0002-3612-7818
authors.initials: Y
authors.lastname: Wu
mesh_terms: D000293:Adolescent; D000375:Aging / Q000502:physiology*; D000465:Algorithms; D002648:Child; D002657:Child Development / Q000502:physiology*; D002675:Child, Preschool; D019277:Entropy; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D016138:Walking / Q000502:physiology