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Proceeding Paper

Recognizing Eating Activities in Free-Living Environment Using Consumer Wearable Devices

1
National Institute of Electrical Engineering, Electronics, Computer Science, Hydraulics and Telecommunications (INP-ENSEEIHT), 31000 Toulouse, France
2
Faculty of Engineering, Kyoto University of Advanced Science (KUAS), Kyoto 621-8555, Japan
3
Institute of Industrial Science, The University of Tokyo, Tokyo 113-8654, Japan
*
Author to whom correspondence should be addressed.
Presented at the 8th International Symposium on Sensor Science, 17–28 May 2021; Available online: https://i3s2021dresden.sciforum.net/.
Academic Editors: Gianaurelio Cuniberti and Larysa Baraban
Published: 17 May 2021
The study of eating behavior has become increasingly important due to the alarming high prevalence of lifestyle-related chronic diseases. In this study, we investigated the feasibility of automatic detection of eating events using affordable consumer wearable devices, including Fitbit wristbands, Mi Bands, and the FreeStyle Libre continuous glucose monitor (CGM). Random forest and XGBoost were applied to develop binary classifiers for distinguishing eating and non-eating events. Our results showed that the proposed method can recognize eating events with an average sensitivity of up to 71%. The classifier using random forest with SMOTE resampling exhibited the best overall performance. View Full-Text
Keywords: activity recognition; machine learning; consumer wearables; Fitbit; continuous glucose monitoring activity recognition; machine learning; consumer wearables; Fitbit; continuous glucose monitoring
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MDPI and ACS Style

Bertrand, L.; Cleyet-Marrel, N.; Liang, Z. Recognizing Eating Activities in Free-Living Environment Using Consumer Wearable Devices. Eng. Proc. 2021, 6, 58. https://doi.org/10.3390/I3S2021Dresden-10141

AMA Style

Bertrand L, Cleyet-Marrel N, Liang Z. Recognizing Eating Activities in Free-Living Environment Using Consumer Wearable Devices. Engineering Proceedings. 2021; 6(1):58. https://doi.org/10.3390/I3S2021Dresden-10141

Chicago/Turabian Style

Bertrand, Lauriane, Nathan Cleyet-Marrel, and Zilu Liang. 2021. "Recognizing Eating Activities in Free-Living Environment Using Consumer Wearable Devices" Engineering Proceedings 6, no. 1: 58. https://doi.org/10.3390/I3S2021Dresden-10141

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