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.

用于自动、被动和客观膳食评估的仿人AI系统 不健康的饮食与慢性疾病的风险密切相关，如心血管疾病， 糖尿病和某些癌症。全球疾病负担研究发现， 在前17个风险因素中，不良饮食绝对是人类疾病的头号风险因素。 尽管饮食和健康之间有很强的联系， 被广泛消费。目前，68.5%的美国成年人超重， 发达国家最近美国人预期寿命的下降发出了另一个令人担忧的信号 关于美国人民的健康状况了解与饮食有关的风险因素 影响人们的健康，并找到有效的方法，使他们能够改善生活习惯， 是公共卫生领域最重要的任务之一。不幸的是，现实世界中的饮食评估 设置的实现非常复杂和不准确。我们需要的技术 使研究人员能够在真实的世界环境中轻松准确地评估饮食摄入量， 可以开发有效的干预措施来管理肥胖症和相关的慢性疾病。我们 提出一个生物医学工程项目来解决饮食评估问题， 利用先进的数学建模、可穿戴电子设备和人工 智能 十多年来，我们的研究团队一直在提高评估饮食的能力。我们有 设计了eButton，这是一种固定在胸前衣服上的小型可穿戴设备，能够 客观地和被动地收集基于图像的饮食数据（即，而不依赖于受试者的 自我报告或设备的自愿操作）。我们还开发了算法来计算 食物的体积和营养成分。自从eButton被开发出来以来，它已经被 美国和其他国家的许多研究人员进行了客观和被动的饮食摄入研究， 包括成人和儿童。 尽管过去取得了成功，但仍有两个挥之不去的关键问题， 使用可穿戴设备的客观和被动饮食评估：1）大量手册 研究人员需要努力从视觉上检查图像数据，以识别食物并估计 他们的体积（部分大小），和2）有隐私的关注，研究人员的观看， 参与者的真实生活图像。虽然解决这些问题可以使eButton和其他 可穿戴设备进行大规模的饮食摄入量研究，我们无法找到有效的解决方案 直到最近人工智能（AI）出现。先进的人工智能系统，特别是那些 基于深度学习，可以通过大量标记数据进行训练以产生结果 在许多应用领域中与人产生的那些相当或甚至上级。AI 技术也是膳食评估的有力工具，可能提供理想的解决方案 对于前面提到的两个问题。因此，我们建议开发一种模仿人类的人工智能 从图像中识别食物，估计份量大小，并找到能量和营养的系统 在一个全自动的过程中从数据库中提取值。使用人工智能方法， 研究人员可以查看参与者的真实图像，人工智能系统很好地尊重个人的 因为它被训练成只识别人类的食物，而不是其他东西。 目前，现有人工智能系统的性能受到广泛种类和 人类食物的高度可变性，培训数据不足，难以找到合适的 食品数据库中的营养信息。在本申请中，我们提出了一种新的策略， 使用先进的数学模型为每个研究参与者个性化人工智能系统 个人的食物选择。通过这个个性化步骤，我们设想的人工智能的维度 系统可以大大减少，我们的目标是自动，客观和被动的饮食 评估可以现实地进行。我们还建议改善电子硬件 开发一个仿生摄像头来扩大eButton的视野。最后我们将 在现实世界中使用人类对个性化人工智能系统进行全面评估 科目