Title
Neuro-fuzzy system with particle swarm optimization for classification of physical fitness in school children
Date Issued
2020
Access level
open access
Resource Type
journal article
Author(s)
Luna-Luza G.
Ccama-Yana D.
Gallegos-Valdivia J.
Universidad Nacional de San Agustín de Arequipa
Universidad Católica del Maule
Publisher(s)
Science and Information Organization
Abstract
Physical fitness is widely known to be one of the critical elements of a healthy life. The sedentary attitude of school children is related to some health problems due to physical inactivity. The following article aims to classify the physical fitness in school children, using a database of 1813 children of both sexes, in a range that goes from six to twelve years. The physical tests were flexibility, horizontal jump, and agility that served to classify the physical fitness using neural networks and fuzzy logic. For this, the ANFIS (adaptive network fuzzy inference system) model was used, which was optimized using the Particle Swarm Optimization algorithm. The experimental tests carried out showed an RMSE error of 3.41, after performing 500 interactions of the PSO algorithm. This result is considered acceptable within the conditions of this investigation.
Start page
505
End page
512
Volume
11
Issue
6
Language
English
OCDE Knowledge area
Pediatría
Nutrición, Dietética
Subjects
Scopus EID
2-s2.0-85087825194
Source
International Journal of Advanced Computer Science and Applications
ISSN of the container
2158107X
Sponsor(s)
To the 'Universidad Nacional de San Agustín de Arequipa', who has financed the project "Normative proposal for assess the levels of physical activity of school children in the province of Arequipa", with contract number 15-2016-UNSA for the financing granted to carry out the article.
Sources of information:
Directorio de Producción Científica
Scopus