تقرير
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor
| العنوان: | Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor |
|---|---|
| المؤلفون: | Huang, Yuning, Hassan, Mohamed Abul, He, Jiangpeng, Higgins, Janine, McCrory, Megan, Eicher-Miller, Heather, Thomas, Graham, Sazonov, Edward O, Zhu, Fengqing Maggie |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Multimedia, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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%. Comment: Accepted at CVPRw 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.07827 |
| رقم الأكسشن: | edsarx.2405.07827 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |