BibTeX for papers by David Kotz; for complete/updated list see
https://www.cs.dartmouth.edu/~kotz/research/papers.html

@InProceedings{bi:vision,
  author =        {Shengjie Bi and David Kotz},
  title =         {{Eating detection with a head-mounted video camera}},
  booktitle =     {{Proceedings of the IEEE International Conference on Healthcare Informatics}},
  year =          2022,
  month =         {June},
  pages =         {60--66},
  publisher =     {IEEE},
  copyright =     {IEEE},
  DOI =           {10.1109/ICHI54592.2022.00021},
  URL =           {https://www.cs.dartmouth.edu/~kotz/research/bi-vision/index.html},
  abstract =      {In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9\% and F1 score 78.7\% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or mobile platforms, in terms of computation, memory, and power. We believe this paper is the first work to experiment with video-based (rather than image-based) eating detection in free-living scenarios.},
}