TY  - JOUR
PY  - 2016//
TI  - Analysis and classification of stride patterns associated with children development using gait signal dynamics parameters and ensemble learning algorithms
JO  - BioMed research international
A1  - Wu, Meihong
A1  - Liao, Lifang
A1  - Luo, Xin
A1  - Ye, Xiaoquan
A1  - Yao, Yuchen
A1  - Chen, Pinnan
A1  - Shi, Lei
A1  - Huang, Hui
A1  - Wu, Yunfeng
SP  - ee9246280
EP  - ee9246280
VL  - 2016
IS  - 
N2  - 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. <br><br>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).<p />  <p>Language: en</p>
LA  - en
SN  - 2314-6133
UR  - http://dx.doi.org/10.1155/2016/9246280
ID  - ref1
ER  -