Abstract
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.
Highlights
Findings from recent studies show that the temporal patterns of eating behavior are if not more, important as the nutritional components and the total calories consumed
The results show that without resampling, the performance of XGBoost was statistically similar to random forest
We have of the theeight eightmodels modelswith with different combinations of machine learning algorithms and resampling techniques
Summary
Findings from recent studies show that the temporal patterns of eating behavior are if not more, important as the nutritional components and the total calories consumed. A recent study shows that proper eating patterns—such as controlling all three meals within certain time window, known as restricted feeding—help people control body weight [2]. These findings suggest that when we eat plays an important role in maintaining health and preventing chronic diseases. Understanding the temporal eating patterns can give hints for disease prediction and health intervention
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
