ArticleIEEE access : practical innovations, open solutions2020
Automatic Count of Bites and Chews From Videos of Eating Episodes.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Pooled it
- MediaPipe-Based Activity Analysis by Healthcare Professionals: Method Development and Technology Acceptance-A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- Unveiling overeating patterns within digital longitudinal data on eating behaviors and contexts.NPJ digital medicine · 2025Article
- Video-based Intake Gesture Recognition Using Meal-length Context.ACM transactions on computing for healthcare · 2025Article
- ByteTrack: a deep learning approach for bite count and bite rate detection using meal videos in children.Frontiers in nutrition · 2025Article
- Transforming Big Data into AI-ready data for nutrition and obesity research.Obesity (Silver Spring, Md.) · 2024Review
- Integrated image and sensor-based food intake detection in free-living.Scientific reports · 2024Article
- Rule-based systems to automatically count bites from meal videos.Frontiers in nutrition · 2024Article
- Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.Journal of the Korean Academy of Child and Adolescent Psychiatry · 2024Article
- Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
- What's on your plate? Collecting multimodal data to understand commensal behavior.Frontiers in psychology · 2022Article
- Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration.Sensors (Basel, Switzerland) · 2021Article
- Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium.International journal of environmental research and public health · 2021Article
- The potential of artificial intelligence in enhancing adult weight loss: a scoping review.Public health nutrition · 2021Article
- Voice-assisted Food Recall using Voice Assistants.HCI International 2022 - late breaking papers : 24th International Conference on Human-Computer Interaction, HCII 2022, virtual event, June 26 - July 1, 2022, proceedings. HCI for health, well-being, universal access and healthy aging. ...Article
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3 authors.
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Abstract
Methods for measuring of eating behavior (known as meal microstructure) often rely on manual annotation of bites, chews, and swallows on meal videos or wearable sensor signals. The manual annotation may be time consuming and erroneous, while wearable sensors may not capture every aspect of eating (e.g. chews only). The aim of this study is to develop a method to detect and count bites and chews automatically from meal videos. The method was developed on a dataset of 28 volunteers consuming unrestricted meals in the laboratory under video observation. First, the faces in the video (regions of interest, ROI) were detected using Faster R-CNN. Second, a pre-trained AlexNet was trained on the detected faces to classify images as a bite/no bite image. Third, the affine optical flow was applied in consecutively detected faces to find the rotational movement of the pixels in the ROIs. The number of chews in a meal video was counted by converting the 2-D images to a 1-D optical flow parameter and finding peaks. The developed bite and chew count algorithm was applied to 84 meal videos collected from 28 volunteers. A mean accuracy (±STD) of 85.4% (±6.3%) with respect to manual annotation was obtained for the number of bites and 88.9% (±7.4%) for the number of chews. The proposed method for an automatic bite and chew counting shows promising results that can be used as an alternative solution to manual annotation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.