Eating detection with a head-mounted video camera
Author ORCID Identifier
David Kotz: https://orcid.org/0000-0001-7411-2783
Document Type
Conference Paper
Publication Date
6-2022
Publication Title
Proceedings of the IEEE International Conference on Healthcare Informatics
Department
Department of Computer Science
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.
DOI
10.1109/ICHI54592.2022.00021
Original Citation
Shengjie Bi and David Kotz. Eating detection with a head-mounted video camera. Proceedings of the IEEE International Conference on Healthcare Informatics, pages 60–66. IEEE, June 2022. doi:10.1109/ICHI54592.2022.00021.
Dartmouth Digital Commons Citation
Bi, Shengjie and Kotz, David, "Eating detection with a head-mounted video camera" (2022). Dartmouth Scholarship. 4293.
https://digitalcommons.dartmouth.edu/facoa/4293
