A Human-Mimetic AI System for Automatic, Passive and Objective Dietary Assessment

用于自动、被动和客观饮食评估的仿人人工智能系统

• 批准号: 10320465
• 负责人: MINGUI SUN
• 金额: $ 65.78万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320465
• 关键词: Address; Adopted; Adult; Affect; Algorithms; American; Artificial Intelligence; Biomedical Engineering; Biomimetics; Cardiovascular Diseases; Cellular Phone; Centers for Disease Control and Prevention (U.S.); Cessation of life; Chest; Child; Chronic Disease; Complex; Consumption; Country; Data; Databases; Developed Countries; Devices; Diabetes Mellitus; Diet; Dietary Assessment; Dietary intake; Dietetics; Dimensions; Eating; Evaluation; Expert Systems; Eye; Feedback; Food; Food Energy; Future; Goals; Gold; Habits; Health; Health care facility; Healthcare; Heart Diseases; Human; Image; Individual; Intake; Label; Life; Life Expectancy; Life Style; Link; Malignant Neoplasms; Manuals; Modeling; Nutrient; Nutritional; Nutritional Science; Obesity; Output; Participant; Patient Self-Report; Performance; Persons; Play; Privacy; Problem Solving; Process; Public Health; Records; Reporting; Research; Research Personnel; Risk; Risk Factors; Role; Shapes; Signal Transduction; System; Technology; Training; Unhealthy Diet; Update; Volition; artificial intelligence algorithm; base; burden of illness; cancer type; computerized data processing; convolutional neural network; deep learning; design; dietary; effective intervention; field study; good diet; human disease; human subject; improved; infancy; mathematical model; microelectronics; mimetics; neural network; obesity management; operation; overweight adults; robotic system; success; tool; validation studies; wearable device

A Human-Mimetic AI System for Automatic, Passive and Objective Dietary Assessment Unhealthy diet is strongly linked to risks of chronic diseases, such as cardiovascular diseases, diabetes and certain types of cancer. The Global Burden of Disease Study has found that, among the top 17 risk factors, poor diet is overwhelmingly the No. 1 risk factor for human diseases. Despite the strong connection between diet and health, unhealthy foods with large portion sizes are widely consumed. Currently, 68.5% of U.S. adults are overweight, among the highest in developed countries. The recent decline in U.S. life expectancy sent another alarming signal about the general health of the American people. Understanding how the diet-related risk factors affect people’s health and finding effective ways to empower them in improving lifestyle habits are among the most important tasks in public health. Unfortunately, dietary assessment in real-world settings has been exceedingly complex and inaccurate to implement. Technology is needed that allows researchers to assess dietary intake easily and accurately in real world settings so that effective intervention to manage obesity and related chronic diseases can be developed. We propose a biomedical engineering project to address the dietary assessment problem, taking advantage of advanced mathematical modeling, wearable electronics and artificial intelligence. Our research team has been improving the ability to assess diet for over a decade. We have designed the eButton, a small wearable device pinned on clothes in front of the chest, capable of collecting image-based dietary data objectively and passively (i.e., without depending on subject’s self-report or volitional operation of the device). We have also developed algorithms to compute food volumes and nutrients from images. Since the eButton was developed, it has been used by many researchers in the U.S. and other countries for objective and passive diet-intake studies in both adults and children. Despite the past successes, there have been two lingering critical problems associated with the objective and passive dietary assessment using wearable devices: 1) substantial manual efforts are required for researchers to visually examine image data to identify foods and estimate their volumes (portion sizes), and 2) there are privacy concerns about researchers’ viewing of participants’ real-life images. Although solving these problems could enable the eButton and other wearable devices for large-scale diet-intake studies, we were not able to find effective solutions until recently when Artificial intelligence (AI) emerged. Advanced AI systems, especially those based on deep learning, can be trained by large amounts of labeled data to produce results comparable or even superior to those produced by human in numerous fields of applications. AI technology is also a powerful tool for dietary assessment, potentially providing an ideal solution to the two previously mentioned problems. We thus propose to develop a human-mimetic AI system to recognize foods from images, estimate portion sizes, and find energy and nutrient values from a database in a fully automatic process. Using the AI approach, there will be no need for researchers to view participants’ real-life images, and the AI system well-respects individuals’ privacy because it is trained to recognizes human foods only, nothing else. Currently, the performances of existing AI systems are limited by the extensive variety and high variability of human foods, insufficient training data, and difficulty in finding appropriate nutritional information from food databases. In this application, we propose a new strategy to personalize the AI system for each research participant using an advanced mathematical model of personal food choices. With this personalization step, the dimensionality of our envisioned AI system can be reduced drastically, and our goal of automatic, objective and passive dietary assessment can be reached realistically. We also propose to improve the electronic hardware and develop a biomimetic camera to enlarge the field of view for the eButton. Finally, we will conduct a thorough evaluation of the personalized AI system in real-world settings using human subjects.

一种用于自动，被动和客观饮食评估的人类模仿AI系统 不健康的饮食与慢性疾病的风险（例如心血管疾病）密切相关， 糖尿病和某些类型的癌症。全球疾病的负担研究发现， 前17个危险因素，饮食不良是人类疾病的第一危险因素。 尽管饮食与健康之间有着密切的联系，但不健康的食物具有很大的尺寸 被广泛消耗。目前，美国68.5％的美国成年人超重，其中最高 发达国家。美国最近的预期寿命下降发出了另一个令人震惊的信号 关于美国人民的一般健康。了解与饮食相关的风险因素如何 影响人们的健康，并找到有效的方法来赋予他们改善生活方式习惯的能力 公共卫生中最重要的任务之一。不幸的是，现实世界中的饮食评估 设置非常复杂，无法实施。需要技术 允许研究人员在现实世界中轻松，准确地评估饮食摄入量，以便 可以开发有效管理肥胖和相关慢性疾病的干预措施。我们 提案一个生物医学工程项目，以解决饮食评估问题， 利用高级数学建模，可穿戴电子设备和人造 智力。 我们的研究团队已经提高了十多年来评估饮食的能力。我们有 设计了Ebutton，这是一种固定在胸前的衣服上的小型可穿戴设备，能够 客观和被动地收集基于图像的饮食数据（即，不依赖于受试者的 设备的自我报告或意志操作）。我们还开发了计算算法 图像中的食物量和营养。自从开发ebutton以来，它已被使用 美国和其他国家的许多研究人员在客观和被动的饮食感染研究中 成人和儿童。 尽管取得了过去的成功，但与 使用可穿戴设备的客观和被动饮食评估：1）大量手册 研究人员需要精力检查图像数据以识别食物并估计 它们的数量（部分尺寸），以及2）关于研究人员观看的隐私问题 参与者的真实图像。尽管解决这些问题可以使Ebutton和其他 可穿戴设备用于大规模饮食量研究，我们无法找到有效的解决方案 直到最近才出现人工智能（AI）。高级AI系统，尤其是那些 基于深度学习，可以通过大量标记数据培训以产生结果 与人类在众多应用领域中所产生的那些相当甚至优越。人工智能 技术也是饮食评估的强大工具，有可能提供理想的解决方案 前面提到的两个问题。因此，我们建议开发人类模仿AI 从图像，估计份量估计食物并找到能量和养分的系统 在全自动过程中来自数据库的值。使用AI方法，将不需要 供研究人员查看参与者的真实图像，而AI系统非常重视个人 隐私是因为它经过训练，只能识别人类食品，别无其他。 目前，现有AI系统的性能受到广泛种类的限制， 人类食物的高度差异，培训数据不足以及难以找到合适的 食品数据库的营养信息。在此应用程序中，我们提出了一种新的策略 使用高级数学模型来个性化每个研究的AI系统 个人食物的选择。通过这个个性化步骤，我们设想的AI的维度 可以大幅度降低系统，我们的目标是自动，客观和被动饮食 可以现实地进行评估。我们还建议改善电子硬件 并开发一个仿生相机，以扩大Ebutton的视野。最后，我们会的 使用人类在现实环境中对个性化的AI系统进行彻底评估 主题。