PDF(Pubmed)
Abstract：
Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.
摘要：
糖尿病已成为全球健康危机，影响约5.37亿成年人。保持血糖需要仔细观察饮食，身体活动,并在必要时坚持药物治疗。从历史上看，饮食监测涉及保存食物日记；然而，这个过程可能是劳动密集型的，和食物的回忆可能会引入错误。食品图像识别系统（FIRS）等自动化技术可以利用计算机视觉和移动摄像头来减轻日记的负担并改善饮食跟踪。这些工具提供各种水平的饮食分析，并为改善膳食的营养质量提供了进一步的建议。当前的研究是对基于移动计算机视觉的食物分类方法的系统回顾，体积估算，和营养估算。对过去二十年发表的相关文章进行了评估，并探讨了与FIRS相关的未来方向和问题。
