Neuro-fuzzy system with particle swarm optimization for classification of physical fitness in school children

Jose Sulla-Torres, Gonzalo Luna-Luza, Doris Ccama-Yana, Juan Gallegos-Valdivia, Marco Cossio-Bolaños

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)505-512
Number of pages8
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number6
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© Science and Information Organization.

Keywords

  • ANFIS
  • Classification
  • Particle swarm optimization
  • Physical fitness
  • RMSE

Fingerprint

Dive into the research topics of 'Neuro-fuzzy system with particle swarm optimization for classification of physical fitness in school children'. Together they form a unique fingerprint.

Cite this