4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"

Research Article

A Method for Estimating Hunger Degree based on Meal and Exercise Logs

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  • @INPROCEEDINGS{10.4108/icst.mobihealth.2014.257389,
        author={Isamu Sugita and Morihiko Tamai and Yutaka Arakawa and Keiichi Yasumoto},
        title={A Method for Estimating Hunger Degree based on Meal and Exercise Logs},
        proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"},
        publisher={IEEE},
        proceedings_a={MOBIHEALTH},
        year={2014},
        month={12},
        keywords={hunger degree estimation blood glucose level estimation non-invasive method meal and exercise information machine learning mobile application},
        doi={10.4108/icst.mobihealth.2014.257389}
    }
    
  • Isamu Sugita
    Morihiko Tamai
    Yutaka Arakawa
    Keiichi Yasumoto
    Year: 2014
    A Method for Estimating Hunger Degree based on Meal and Exercise Logs
    MOBIHEALTH
    IEEE
    DOI: 10.4108/icst.mobihealth.2014.257389
Isamu Sugita1,*, Morihiko Tamai1, Yutaka Arakawa1, Keiichi Yasumoto1
  • 1: Nara Institute of Science and Technology
*Contact email: sugita.isamu.sa3@is.naist.jp

Abstract

If temporal variation of a person's hunger degree could be estimated, it would be possible to adjust his/her eating habits and/or prevent obesity. It is well-known that there is a negative correlation between a hunger degree and a blood glucose level. However, it is hard to measure a person's blood glucose level anytime and anywhere, because it relies usually on an invasive method (e.g., blood sampling). This paper proposes a method for estimating a person's hunger degree in a non-invasive way. Our proposed method is composed of (1) a blood glucose level estimation model based on logs of meals and exercises, and (2) a hunger degree estimation model based on the estimated glucose level. The former model is constructed by correlating an actual blood glucose level and logs of meals and exercises with a machine learning technique. Here, the actual blood glucose level is measured by a commercial blood glucose meter invasively. The latter model is constructed by associating the measured blood glucose level with a subjective hunger degree. We also design and develop a mobile application for facilitating a user to easily record meals and exercises information. Through an experiment with a subject, we confirmed that our system can estimate a blood glucose level within about 14% mean percentage error and finally estimate hunger degree within about 1.3 levels mean error among 10 levels.