Abstract
With the prevalence of lifestyle diseases, systems for monitoring meal information through the Internet of Things have become widespread. Studies have shown that the sequence and time of meals are considered to be part of the factors that affect lifestyle diseases such as diabetes and obesity. An improved smart tableware is proposed which can detect the process of meal information more effectively. Since meal information is detected sequentially, the method of using recurrent neural network to detect the meal sequence is introduced, and the feasibility of this method is demonstrated by experiments. After analyzing information obtained from tableware, meal information will be fed back to users and help to improve their eating habits. In the meal experiment, we used improved smart tableware and proved the feasibility of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
MedicineNet.com, Medical Definition of lifestyle disease. https://www.medicinenet.com/script/main/art.asp?articlekey=38316. Accessed 10 July 2020
World Health Organization: Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 10 July 2020
World Health Organization: Obesity. https://www.who.int/health-topics/obesity#tab=tab_1. Accessed 10 July 2020
Sami, W., Ansari, T., Butt, N.S., Ab Hamid, M.R.: Effect of diet on type 2 diabetes mellitus: a review. Int. J. Health Sci. 11(2), 65–71 (2017)
Day, N.E., McKeown, N., Wong, M.Y., Welch, A., Bingham, S.: Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int. J. Epidemiol. 30(2), 309–317 (2001)
Wohlers, E.M., Sirard, J.R., Barden, C.M., Moon, J.K.: Smart phones are useful for food intake and physical activity surveys. In: Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5183–5186 (2009)
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)
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)
Farooq, M., Sazonov, E.: A novel wearable device for food intake and physical activity recognition. Sensors (Basel) 16(7), 1067 (2016)
Kalantarian, H., Alshurafa, N., Le, T., Sarrafzadeh, M.: Monitoring eating habits using a piezoelectric sensor-based necklace. Comput. Biol. Med. 58, 46–55 (2015)
Aizawa, K., Ogawa, M.: FoodLog: multimedia tool for healthcare applications. IEEE Multimed. 22(2), 4–8 (2015)
Bin Kassim, M.F., Mohd, M.N.H.: Food intake gesture monitoring system based-on depth sensor. Bull. Electr. Eng. Inform. 8(2), 470–476 (2019)
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)
Imai, S., Matsuda, M., Hasegawa, G., et al.: A simple meal plan of ‘eating vegetables before carbohydrate’ was more effective for achieving glycemic control than an exchange-based meal plan in Japanese patients with type 2 diabetes. Asia Pac. J. Clin. Nutr. 20(2), 161–168 (2011)
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, vol. 66, pp.503– 508 (2016)
Zhang, L., Kaiya, K., Suzuki, H., Koyama, A.: A smart tableware-based meal information collection system using machine learning. Int. J. Web Grid Serv. 15(2), 206–218 (2019)
Zhang, L., Kaiya, K., Suzuki, H., Koyama, A.: Meal information recognition based on smart tableware using multiple instance learning. In: Proceedings of 22nd International Conference on Network-Based Information Systems, NBiS-2019, AISC, vol. 1036, pp. 189–199 (2019)
Chung, J., Gülçehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555 (2014)
Paszke, A., et al: Pytorch: an imperative style, high-performance deep learning library. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, L., Suzuki, H., Koyama, A. (2021). Detection and Analysis of Meal Sequence and Time Based on Internet of Things. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-61105-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61104-0
Online ISBN: 978-3-030-61105-7
eBook Packages: EngineeringEngineering (R0)