ArticleBioengineering (Basel, Switzerland)2022
Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data.IEEE open journal of engineering in medicine and biology · 2025Article
- Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity.Journal of diabetes science and technology · 2024Article
- A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring.Scientific reports · 2024Article
- Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.IEEE journal of biomedical and health informatics · 2024Article
- Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study.JMIR mHealth and uHealth · 2023Article
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2 authors.
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Abstract
In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated them to identify meals. The novelty of our approach is that we analyze a much longer window (0.5-15 min) using a convolutional neural network. Longer windows can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We test our methods on the public Clemson all-day dataset, which consists of 354 recordings containing 1063 eating episodes. We found that accuracy at detecting eating increased by 15% in ≥4 min windows compared to ≤15 s windows. Using a 6 min window, we detected 89% of eating episodes, with 1.7 false positives for every true positive (FP/TP). These are the best results achieved to date on this dataset.
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