Meal Information Recognition Based on Smart Tableware Using Multiple Instance Learning

  • Liyang ZhangEmail author
  • Kohei Kaiya
  • Hiroyuki Suzuki
  • Akio Koyama
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)


In recent years, people are paying more and more attention to the management of healthy meals because of the influence of lifestyle-related diseases, and some diet management systems are trying to help people lead a healthy life. Moreover, some studies have found that the proper meal habits can play a role in preventing disease to some extent. This paper introduces the smart tableware consisting of an acceleration sensor and a pressure sensor, which is used to obtain information about the sequence and content of meals. In addition, a method of analyzing and processing meal information through Multiple Instance Learning (MIL) is proposed to help people prevent diseases that are affected by lifestyle habits. At the same time, the acquisition process of MIL dataset is introduced and Support Vector Machine (SVM) is used. The performance evaluation results show that good results can be achieved by using MIL.


  1. 1. Medical definition of lifestyle disease. Accessed 8 May 2019
  2. 2.
    World Health Organization: Obesity and overweight. Accessed 8 May 2019
  3. 3.
    World Health Organization: Obesity. Accessed 8 May 2019
  4. 4.
    World Health Organization: The top 10 causes of death. Accessed 8 May 2019
  5. 5.
    World Health Organization: Cardiovascular disease. Accessed 8 May 2019
  6. 6.
    Sazonov, E.S., Fontana, J.M.: A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sens. J. 12(5), 1340–1348 (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, S., Zhou, G., Hu, L., Chen, Z., Chen, Y.: CARE: chewing activity recognition using noninvasive single axis accelerometer. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, pp. 109–112 (2015)Google Scholar
  8. 8.
    Farooq, M., Sazonov, E.: A novel wearable device for food intake and physical activity recognition. Sensors (Basel) 16(7), 1067 (2016)CrossRefGoogle Scholar
  9. 9.
    Dong, Y., Scisco, J., Wilson, M., Muth, E., Hoover, A.: Detecting periods of eating during free-living by tracking wrist motion. IEEE J. Biomed. Health Inform. 18(4), 1253–1260 (2014)CrossRefGoogle Scholar
  10. 10.
    Aizawa, K., Ogawa, M.: FoodLog: multimedia tool for healthcare applications. IEEE Multimedia 22(2), 4–8 (2015)CrossRefGoogle Scholar
  11. 11.
    Foo.log Inc: FoodLog. Accessed 8 May 2019
  12. 12.
    Noronha, J., Hysen, E., Zhang, H., Gajos, K.Z.: PlateMate: crowdsourcing nutrition analysis from food photographs. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, UIST 2011, pp. 1–12 (2011)Google Scholar
  13. 13.
    Kuwata, H., Iwasaki, M., et al.: Meal sequence and glucose excursion, gastric emptying and incretin secretion in type 2 diabetes: a randomized, controlled crossover, exploratory trial. Diabetologia 59(3), 453–461 (2016)CrossRefGoogle Scholar
  14. 14.
    Kaiya, K., Koyama, A.: Design and implementation of meal information collection system using IoT wireless tags. In: Proceedings of 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2016), CISIS 2016.66, pp. 503–508 (2016)Google Scholar
  15. 15.
    Kaiya, K., Suzuki, H., Koyama, A.: Meal Information Collection System Using Smart Tableware. The Institute of Electronics, Information and Communication Engineers, MVE2017-20, pp. 31–36 (2017)Google Scholar
  16. 16.
    Medical Xpress: Chewing habits determine blood sugar levels after a carbohydrate-rich meal. Accessed 8 May 2019
  17. 17.
    Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)CrossRefGoogle Scholar
  18. 18.
    Kyritsis, K., Tatli, C.L., Diou, C., Delopoulos, A.: Automated analysis of in meal eating behavior using a commercial wristband IMU sensor. In: Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843–2846 (2017)Google Scholar
  19. 19.
    Zhou, Z.-H., Zhang, M.-L., Huang, S.-J., Li, Y.-F.: MIML: a framework for learning with ambiguous objects. CoRR abs/0808.3231 (2008)Google Scholar
  20. 20.
    Bunescu, R.C., Mooney, R.J.: Multiple instance learning for sparse positive bags. In: Proceedings of the 24th International Conference on Machine Learning. Corvallis, OR, pp. 105–112 (2007)Google Scholar
  21. 21.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Liyang Zhang
    • 1
    Email author
  • Kohei Kaiya
    • 1
  • Hiroyuki Suzuki
    • 1
  • Akio Koyama
    • 1
  1. 1.Departmnet of Informatics, Graduate School of Science and EngineeringYamagata UniversityYonezawa-shiJapan

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