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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Through Innovations in Mobile and Wireless Technologies", MOBIHEALTH 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 11-14 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781631900143 |
| DOIs | |
| Publication status | Published - Jan 20 2015 |
| Externally published | Yes |
| Event | 4th International Conference on Wireless Mobile Communication and Healthcare, MOBIHEALTH 2014 - Athens, Greece Duration: Nov 3 2014 → Nov 5 2014 |
Publication series
| Name | Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Through Innovations in Mobile and Wireless Technologies", MOBIHEALTH 2014 |
|---|
Conference
| Conference | 4th International Conference on Wireless Mobile Communication and Healthcare, MOBIHEALTH 2014 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 11/3/14 → 11/5/14 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Informatics
Cite this
A method for estimating hunger degree based on meal and exercise logs. / Sugita, Isamu; Tamai, Morihiko; Arakawa, Yutaka; Yasumoto, Keiichi.
Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Through Innovations in Mobile and Wireless Technologies", MOBIHEALTH 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 11-14 7015896 (Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Through Innovations in Mobile and Wireless Technologies", MOBIHEALTH 2014).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A method for estimating hunger degree based on meal and exercise logs
AU - Sugita, Isamu
AU - Tamai, Morihiko
AU - Arakawa, Yutaka
AU - Yasumoto, Keiichi
PY - 2015/1/20
Y1 - 2015/1/20
N2 - 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.
AB - 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.
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U2 - 10.1109/MOBIHEALTH.2014.7015896
DO - 10.1109/MOBIHEALTH.2014.7015896
M3 - Conference contribution
AN - SCOPUS:84925372846
T3 - Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Through Innovations in Mobile and Wireless Technologies", MOBIHEALTH 2014
SP - 11
EP - 14
BT - Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming Healthcare Through Innovations in Mobile and Wireless Technologies", MOBIHEALTH 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -