Chinese Journal of Tissue Engineering Research ›› 2012, Vol. 16 ›› Issue (37): 6956-6960.doi: 10.3969/j.issn.2095-4344.2012.37.024

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Modeling of a comprehensive fitness evaluation model based on artificial neural network

Zhang Chong-lin1, 2, Yu Li-juan1, Wu Wei-bing1   

  1. 1School of Kinesiology of Shanghai University of Sport, Shanghai 200438, China
    2Institute of Sports of Jinggangshan University, Ji’an 343009, Jiangxi Province, China
  • Received:2012-06-04 Revised:2012-07-11 Online:2012-09-09 Published:2012-09-09
  • Contact: Yu Li-juan, Doctor, Professor, Doctoral supervisor, School of Kinesiology of Shanghai University of Sport, Shanghai 200438, China ljyu@shou.edu.cn
  • About author:Zhang Chong-lin☆, Doctor, Lecturer, School of Kinesiology of Shanghai University of Sport, Shanghai 200438, China; Institute of Sports of Jinggangshan University, Ji’an 343009, Jiangxi Province, China ccnuzcl@163.com

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Abstract:

BACKGROUND: Many scholars agree that different fitness indexes play different roles in the comprehensive evaluation of the fitness. But there is no effective way to build a comprehensive fitness evaluation model.
OBJECTIVE: To establish the comprehensive fitness evaluation model based on artificial neural network.
METHODS: The university faculties in Shanghai were selected as objects and the artificial neural network technology was used to establish the comprehensive fitness evaluation models of second-rank index and third-rank index in different ages ad genders.
RESULTS AND CONCLUSION: The fitting degrees of the second-rank index and third-rank index comprehensive fitness evaluation models established in this experiment were ＞ 93% and ＞ 94%. The weight calculation results are reliable. In this model, the indicators reflecting body diathesis accounted for the highest proportion of weight coefficients, followed by the indicators reflecting the body shape, and the indicators reflecting the physical function accounted for the lowest proportion of weight coefficients. The findings confirmed that the comprehensive fitness evaluation model was successfully established by using the artificial neural network technology.

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