Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging problem where human-based reviewing can be tedious, and algorithm-based review suffers from data imbalance and perceptual aliasing problems. To address these issues, we propose a neural network-based method with a two-stage training framework that tactfully combines fine-tuning and transfer learning techniques. Our method is evaluated on a newly collected dataset called ``UA Free Living Study", which uses an egocentric wearable camera, AIM-2 sensor, to simulate food consumption in free-living conditions. The proposed training framework is applied to common neural network backbones, combined with approaches in the general imbalanced classification field. Experimental results on the collected dataset show that our proposed method for automatic ingestion environment recognition successfully addresses the challenging data imbalance problem in the dataset and achieves a promising overall classification accuracy of 96.63%.
@article{arxiv.2405.07827,
title = {Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor},
author = {Yuning Huang and Mohamed Abul Hassan and Jiangpeng He and Janine Higgins and Megan McCrory and Heather Eicher-Miller and Graham Thomas and Edward O Sazonov and Fengqing Maggie Zhu},
journal= {arXiv preprint arXiv:2405.07827},
year = {2024}
}