• DocumentCode
    3117344
  • Title

    A fuzzy logic approach to predict human body weight based on AR model

  • Author

    Tanii, Hideaki ; Nakajima, Hiroshi ; Tsuchiya, Naoki ; Kuramoto, Kei ; Kobashi, Syoji ; Hata, Yutaka

  • Author_Institution
    Grad. Sch. of Eng., Univ. of Hyogo, Kobe, Japan
  • fYear
    2011
  • fDate
    27-30 June 2011
  • Firstpage
    1022
  • Lastpage
    1025
  • Abstract
    This paper proposes a body weight prediction method using auto regressive (AR) model and Fuzzy-AR model. First, we employ 6 persons body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike´s Information Criterion. In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.798 in correlation coefficient between predicted and truth values. Second, we propose a Fuzzy-AR model that predicts body weight of next p days from last p days, where p is the order of AR model. In this method, we propose a Fuzzy-AR model with the fuzzy membership function using last p days data. In the experiment, the Fuzzy-AR model obtained 0.558 in correlation coefficient on 2 subjects.
  • Keywords
    autoregressive processes; correlation methods; fuzzy logic; health care; prediction theory; time series; auto regressive model; body weight prediction method; correlation coefficient; fuzzy logic approach; fuzzy membership function; fuzzy-AR model; human body weight; information criterion; predicted body weight change; predicted values; time-series data; truth values; Brain modeling; Correlation; Data models; Mathematical model; Predictive models; Weight measurement; autoregressive model; body weight; healthcare system; prediction model; time-series data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
  • Conference_Location
    Taipei
  • ISSN
    1098-7584
  • Print_ISBN
    978-1-4244-7315-1
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2011.6007361
  • Filename
    6007361