• DocumentCode
    3636132
  • Title

    Artificial neural networks for aerobic fitness approximation

  • Author

    K. Vainamo;S. Nissila;T. Makikalio;M. Tulppo;J. Roning

  • Author_Institution
    Dept. of Electr. Eng., Oulu Univ., Finland
  • Volume
    4
  • fYear
    1996
  • Firstpage
    1939
  • Abstract
    A unique method for approximating aerobic fitness from demographic and heart rate variables using an artificial neural network (ANN) approximation is proposed. Conventional oxygen uptake measurement methods are expensive and require special clinical instruments. The present method is based on a structure of two ANNs connected in a serial fashion. The first ANN structure is called a preclassifier. It has inputs of physiological features and fuzzy features identified in the material used. The latter ANN structure is a primary approximator, which has an input of the preclassifier result and certain statistical features calculated from the heart rate recordings.
  • Keywords
    "Artificial neural networks","Testing","Heart rate","Heart rate variability","Bicycles","Protocols","Demography","Instruments","Fuzzy logic","Time measurement"
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1996., IEEE International Conference on
  • Print_ISBN
    0-7803-3210-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1996.549198
  • Filename
    549198