ArticleSensors (Basel, Switzerland)2020
Retrieval and Timing Performance of Chewing-Based Eating Event Detection in Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 22 citations in OpenAlex.
- Automated detection of chewing movements in videofluoroscopic swallowing studies using deep learning for landmark detection and motion analysis.Computers in biology and medicine · 2026Article
- Special Issue "Advanced Signal Processing in Wearable Sensors for Health Monitoring".Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living and compared the bottom-up approach against two top-down algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection performance. In 122 hours of free-living EMG data from 10 participants, a total of 44 eating occasions were detected, with a maximum F1 score of 99.2%. Average detection timing errors of the bottom-up algorithm were 2.4 ± 0.4 s and 4.3 ± 0.4 s for the start and end of eating occasions, respectively. Our bottom-up algorithm has the potential to work with different wearable sensors that provide chewing cycle data. We suggest that the research community report timing errors (e.g., using the metrics described in this work).
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