The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior. These allow the study of eating behavior outside the laboratory in free-living conditions, without the need for video recordings and laborious manual annotations. In this paper, we present a high-level overview of our recent work on intake monitoring using a smartwatch, as well as methods using an in-ear microphone. We also present evaluation results of these methods in challenging, real-world datasets. Furthermore, we discuss use-cases of such intake monitoring tools for advancing research in eating behavior, for improving dietary monitoring, as well as for developing evidence-based health policies. Our goal is to inform researchers and users of intake monitoring methods regarding (i) the development of new methods based on commercially available devices, (ii) what to expect in terms of effectiveness, and (iii) how these methods can be used in research as well as in practical applications.
翻译:过去十年来,人工智能和机器学习算法的进展使得能够为客观计量饮食开发新的方法,包括衡量饮食状况和计量饮食行为,从而可以研究实验室外在免费生活条件下的饮食行为,而不需要录像和艰苦的人工说明。在本文中,我们用智能观察以及使用近距离麦克风对最近有关摄入监测的工作进行高级别概述。我们还介绍了这些方法在具有挑战性的、真实世界数据集方面的评价结果。此外,我们讨论了利用这些摄入监测工具推进饮食行为研究、改进饮食监测以及制定循证保健政策的情况。我们的目标是向研究人员和用户通报摄入监测方法:(一) 开发基于商业可用装置的新方法;(二) 在实效方面预期什么,以及(三) 如何将这些方法用于研究以及实际应用。