Research on Intelligent Exercise Prescription System for Civil Servant

  • Qi LuoEmail author
  • Wei Deng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 529)


Public servant refers to the people who work in the government institutions of various levels and execute the mission of state administrative functions and powers. Civil servant is in sub-health status and is high dangerous group of some chronics. Civil servant has a highly recognition about the value of sport for health and has desire to participate in it. But because some objective and subjective reasons, Civil servant cannot participate in exercise and is lack of targeted and scientific. The intelligent exercise prescription system for civil servant has been proposed by the following method such as literature, expert interviews, experimental test, software engineering method, data mining, system dynamics modeling. The intelligent exercise prescription system based theory on artificial intelligence and assessment of fitness-health include these achievements. The intelligent exercise prescription system is the life-style and rest/work system and chronics of civil servant taken into account deeply so as to ensure the feasibility and targeted and scientific of exercise prescription.


Decision Support System Civil Servant Expert Interview Database Management System System Dynamic Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This paper work is supported by 2014 Youth Scientific Research Foundation of Hubei Province Education Department (No. Q20144102, Research on Hubei Provincial Civil Servant Health Promotion and Management System).


  1. 1.
    Keen, P.: Decision support systems: A Research Perspective. Center for Information Systems Research, Alfred P. Sloan School of Management, Cambridge (1980)Google Scholar
  2. 2.
    Sprague, R.A.: Framework for the development of decision support systems. MIS Q. 4(4), 1–25 (1980)CrossRefGoogle Scholar
  3. 3.
    Haag, S., Cummings, M., McCubbrey, D.J., Pinsonneault, A., Donovan, R.: Management Information Systems: For the Information Age, pp. 136–140. McGraw-Hill Ryerson Limited, New York (2000)Google Scholar
  4. 4.
    Wright, A., Sittig, D.: A framework and model for evaluating clinical decision support architectures q. J. Biomed. Inform. 41, 982–990 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Sports Engineering and Information TechnologyWuhan Sports UniversityWuhanChina

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