Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor

• URL: http://arxiv.org/abs/2405.07827v1
• Date: Mon, 13 May 2024 15:12:21 GMT
• Title: Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor
• Authors: Yuning Huang, Mohamed Abul Hassan, Jiangpeng He, Janine Higgins, Megan McCrory, Heather Eicher-Miller, Graham Thomas, Edward O Sazonov, Fengqing Maggie Zhu,
• Abstract summary: 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. 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%.
• Score: 3.9956522522260447
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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%.

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