Dietary Assessment
Dietary assessment, the process of measuring food and nutrient intake, is undergoing a transformation driven by advancements in artificial intelligence. Current research focuses on automating this process using image-based analysis, employing convolutional neural networks, generative adversarial networks (GANs), and other deep learning architectures to recognize foods, estimate portion sizes, and calculate nutritional content from photographs or video. These automated methods aim to overcome the limitations of traditional self-reporting methods, which are prone to bias and inaccuracy, ultimately improving the accuracy and efficiency of nutritional monitoring for individuals and populations. This has significant implications for public health, personalized nutrition, and the management of conditions like malnutrition.
Papers
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning
Jiachuan Peng, Peilun Shi, Jianing Qiu, Xinwei Ju, Frank P. -W. Lo, Xiao Gu, Wenyan Jia, Tom Baranowski, Matilda Steiner-Asiedu, Alex K. Anderson, Megan A McCrory, Edward Sazonov, Mingui Sun, Gary Frost, Benny Lo
Image Based Food Energy Estimation With Depth Domain Adaptation
Gautham Vinod, Zeman Shao, Fengqing Zhu