Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network

• Published in: Bioengineering (Basel) Vol. 9; no. 2; p. 70
• Main Authors: Sharma, Surya, Hoover, Adam
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.02.2022; MDPI
• Subjects: Accelerometers; Artificial neural networks; automated dietary monitoring; Bioengineering; Datasets; Deep learning; Eating; eating detection; Food; Food consumption; Ingestion; m-health; Meals; Methods; Neural networks; obesity; Probability; Sensors; Smartwatches; Wearable computers; wearables; Wrist; m-health; wearables; eating detection; obesity; automated dietary monitoring
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering9020070

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.