Eating detection with a head-mounted video camera
[bi:video-tr]Shengjie Bi and David Kotz. Eating detection with a head-mounted video camera. Technical Report number TR2021-1002, Dartmouth Computer Science, December 2021. ©Copyright the authors.
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.
Citable with [BibTeX]
Keywords: [mhealth] [sensors] [wearable]
Available from the publisher: [page]
Available from the author:
This pdf was produced by the publisher and its posting here is permitted by the publisher.