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

Author:

Bertrand Lauriane,Cleyet-Marrel Nathan,Liang ZiluORCID

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.

Publisher

MDPI AG

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Exploring the Impact of Wearable Continuous Glucose Monitoring on Glucose Regulation and Eating Behavior in Healthy Individuals: A Pilot Study;Proceedings of the Behavior Transformation by IoT International Workshop;2024-06-03

2. Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults;Frontiers in Medical Technology;2022-11-04

3. Correlation Analysis of Nested Consumer Health Data: A New Look at an Old Problem;2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech);2022-03-07

4. Association Rules Mining on Multimodal Quantified-Self Data;2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE);2022-01-29

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