Mobile Food Intake Visualization and Voice Recognize (FIVR)

描述(由申请人提供)： 不充分的饮食摄入量评估工具阻碍了研究饮食和疾病之间的关系。适合用于大型流行病学研究的方法(例如，饮食回忆、食物日记和食物频率问卷)存在相当大的不准确性，而更准确的方法(例如，新陈代谢病房研究、双重标签水)对于基于人口的研究来说，成本高得令人望而却步，而且/或劳动密集型。需要一种简单、廉价和方便但有效的膳食测量工具来更准确地确定人群的膳食摄入量。因此，我们提出了一个分三个阶段的项目，以开发和测试一种名为FIVR(食物摄入量可视化和语音识别器)的新评估工具，该工具使用创新技术的新组合：先进的语音识别、可视化技术和电子系统中的自适应用户建模，以自动记录和评估食物摄入量。Fivr使用手机实时捕捉饮食摄入量的语音记录和照片。这些双重数据源被发送到数据库服务器，用于通过语音识别和图像分析进行实时食物识别和分量大小测量的识别处理。用户模型将允许在系统通过经验学习并适应个人的食物模式时，在仅靠图像可能不能产生准确结果的情况下，增强对食物和准备方法的识别。目标是将现有的语音和图像识别技术融合到一个系统中，该系统将根据食物类型和独特特征识别食物，并通过显示至少两个角度的图像的胶片剪辑确定体积。确定的项目将与食品成分数据库中的适当食品项目和数量相匹配，并计算出营养摄入量。研究人员将能够通过饮食分析程序ProNuta的改编版本查看结果分析。建议的方案将包括三个不同的阶段：1)技术开发、集成和测试；2)控制饮食的有效性测试(代谢病房研究)；以及3)真实世界的效用测试。收集的数据的有效性将通过营养计算与所消耗的新陈代谢病房饮食的已知成分的匹配程度来判断。FIFR有可能建立一种高度准确的膳食摄入量评估方法，适合在人口层面上以成本效益的方式使用，从而推动关键的公共卫生目标。

DESCRIPTION (provided by applicant): Inadequate dietary intake assessment tools hamper studying relationships between diet and disease. Methods suitable for use in large epidemiologic studies (e.g., dietary recall, food diaries, and food frequency questionnaires) are subject to considerable inaccuracy, and more accurate methods (e.g., metabolic ward studies, doubly-labeled water) are prohibitively costly and/or labor-intensive for use in population-based studies. A simple, inexpensive and convenient, yet valid, dietary measurement tool is needed to provide more accurate determination of dietary intake in populations. We therefore propose a three-phase project to develop and test a new assessment tool called FIVR (Food Intake Visualization and Voice Recognizer) that uses a novel combination of innovative technologies: advanced voice recognition, visualization techniques, and adaptive user modeling in an electronic system to automatically record and evaluate food intake. FIVR uses cell phones to capture both voice recordings and photographs of dietary intake in real-time. These dual sources of data are sent to a database server for recognition processing for real-time food recognition and portion size measurement through speech recognition and image analysis. The user model will allow for enhanced identification of food and method of preparation in situations that images alone might not produce accurate results as the system learns through experience and adapts to the individual's food patterns. Objectives are to fuse existing voice and image recognition techniques into a system that will recognize foods by food type and unique characteristics and determine volume by film clip showing an image from at least two angles. The item identified will be matched to an appropriate food item and amount within a food composition database and nutrient intake computed. Researchers will be able to view the resulting analysis through an adapted version of the dietary analysis program, ProNutra. The proposed protocol will incorporate three discrete phases: 1) technology development, integration, and testing; 2) validity testing with a controlled diet (metabolic ward study); and 3) real-world utility testing. Validity of the data collected will be judged by how closely the nutrient calculations match the known composition of the metabolic ward diets consumed. FIVR has the potential to establish a method of highly accurate dietary intake assessment suitable for cost-effective use at the population level, and thereby advance crucial public health objectives.