Karunanithi Mohan
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:742-745. doi: 10.1109/EMBC.2017.8036931.
Diet monitoring is one of the most important aspects in preventative health care that aims to reduce various health risks. Manual recording has been a prevalence among all approaches yet it is tedious and often end up with a low adherence rate. Several existing techniques that have been developed to monitor food intake suffer too with accuracy, efficiency, and user acceptance rate. In this paper we propose a novel approach on measuring food nutrition facts, through a pocket-size non-intrusive near-infrared (NIR) scanner. We build efficient regression models that can make quantitative prediction on food nutrition contents, such as energy and carbohydrate. Our extensive experiments on off-the-shelf liquid foods demonstrates the accuracy of these regression models and proves the applicability of using NIR spectra that are collected by small hand-held scanner, on food nutrition prediction.
饮食监测是预防性医疗保健中最重要的方面之一，旨在降低各种健康风险。手动记录在所有方法中都很普遍，但它很繁琐，而且往往导致依从率很低。现有的几种用于监测食物摄入量的技术在准确性、效率和用户接受率方面也存在问题。在本文中，我们提出了一种通过袖珍式非侵入性近红外（NIR）扫描仪测量食物营养成分的新方法。我们建立了高效的回归模型，可以对食物营养成分进行定量预测，如能量和碳水化合物。我们对现成液体食物进行的大量实验证明了这些回归模型的准确性，并证明了使用小型手持扫描仪收集的近红外光谱进行食物营养预测的适用性。
