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Recognizing Eating Activities in Free-living Environment using Consumer Wearable Sensors
Lauriane Bertrand 1 , Nathan Cleyet-Marrel 1 , Zilu Liang * 2
1  National Institute of Electrical Engineering, Electronics, Computer science, Hydraulics and Telecommunications (INP-ENSEEIHT), Toulouse, France
2  Faculty of Engineering, Kyoto University of Advanced Science (KUAS) Institute of Industrial Science, The University of Tokyo, Kyoto/Tokyo, Japan

10.3390/I3S2021Dresden-10141 (registering DOI)

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 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.

Keywords: activity recognition; machine learning; consumer wearables; fitbit; continuous glucose monitoring