TY - GEN
T1 - Wi-Fi-Enabled Automatic Eating Moment Monitoring Using Smartphones
AU - Lin, Zhenzhe
AU - Xie, Yucheng
AU - Guo, Xiaonan
AU - Wang, Chen
AU - Ren, Yanzhi
AU - Chen, Yingying
N1 - Funding Information: This work was supported by the National Science Foundation Grant CNS-1826647. Publisher Copyright: © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2020
Y1 - 2020
N2 - Dietary habits are closely correlated with people’s health. Study reveals that unhealthy eating habits may cause various diseases such as obesity, diabetes and anemia. To help users create good eating habits, eating moment monitoring plays a significant role. However, traditional methods mainly rely on manual self-report or wearable devices, which either require much user efforts or intrusive dedicated hardware. In this work, we propose a user effort-free eating moment monitoring system by leveraging the WiFi signals extracted from the commercial off-the-shelf (COTS) smartphones. In particular, our system captures the eating activities of users to determine the eating moments. The proposed system can further identify the fine-grained food intake gestures (e.g., eating with fork, knife, spoon, chopsticks and bard hand) to estimate the detailed eating episode for each food intake gesture. Utilizing the dietary information, our system shows the potential to infer the food category and food amount. Extensive experiments with 10 subjects over 400-min eating show that our system can recognize a user’s food intake gestures with up to 97.8% accuracy and estimate the dietary moment within 1.1-s error.
AB - Dietary habits are closely correlated with people’s health. Study reveals that unhealthy eating habits may cause various diseases such as obesity, diabetes and anemia. To help users create good eating habits, eating moment monitoring plays a significant role. However, traditional methods mainly rely on manual self-report or wearable devices, which either require much user efforts or intrusive dedicated hardware. In this work, we propose a user effort-free eating moment monitoring system by leveraging the WiFi signals extracted from the commercial off-the-shelf (COTS) smartphones. In particular, our system captures the eating activities of users to determine the eating moments. The proposed system can further identify the fine-grained food intake gestures (e.g., eating with fork, knife, spoon, chopsticks and bard hand) to estimate the detailed eating episode for each food intake gesture. Utilizing the dietary information, our system shows the potential to infer the food category and food amount. Extensive experiments with 10 subjects over 400-min eating show that our system can recognize a user’s food intake gestures with up to 97.8% accuracy and estimate the dietary moment within 1.1-s error.
KW - Eating moment monitoring
KW - Healthy eating
KW - WiFi sensing
UR - http://www.scopus.com/inward/record.url?scp=85084702963&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084702963&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-42029-1_6
DO - https://doi.org/10.1007/978-3-030-42029-1_6
M3 - Conference contribution
SN - 9783030420284
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 77
EP - 91
BT - IoT Technologies for HealthCare - 6th EAI International Conference, HealthyIoT 2019, Proceedings
A2 - Garcia, Nuno M.
A2 - Pires, Ivan Miguel
A2 - Goleva, Rossitza
PB - Springer
T2 - 6th EAI International Conference on IoT Technologies for HealthCare, HealthyIoT 2019
Y2 - 4 December 2019 through 6 December 2019
ER -