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Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation

  • Martin Vázquez
  • Patricia MelinEmail author
  • German Prado-Arechiga
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 827)

Abstract

In the recent years, the technological advancements tend to leave people with the necessity to stay in a sedentary position for several hours in order to achieve their work objectives. One of the parameters involving health issues tends to do with blood pressure. These are measurements that involve the circulation of the blood throughout the human body. We are developing a modeling and classification of hybrid system to evaluate and see the tendency of how much does a sedentary state affect while working in front of a computer for more than 8 h. The main idea is that the system learns the behavior or the trend of the different blood pressure that are extracted from individuals that fit this experiment. A modular network will be used for the part of modeling, while using fuzzy module to help us determine the best practical classification process in order to obtain a more precise affection of blood pressure.

Keywords

Accelerometer Blood pressure Classification Diastolic pressure Fuzzy system Modeling Modular neural network Neural system Sedentary behavior Sitting time Systolic pressure 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Vázquez
    • 1
  • Patricia Melin
    • 1
    Email author
  • German Prado-Arechiga
    • 2
  1. 1.Tijuana Institute of TechnologyTijuanaMexico
  2. 2.Excel Medical CenterTijuanaMexico

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