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、被动和客观饮食评估的仿人人工智能系统 不健康的饮食与慢性疾病的风险密切相关，例如心血管疾病、 全球疾病负担研究发现，其中包括糖尿病和某些类型的癌症。 在前17位危险因素中，不良饮食绝对是人类疾病的第一大危险因素。 尽管饮食与健康之间存在密切联系，但大份量的不健康食品 目前，68.5% 的美国成年人超重，位居世界前列。 发达国家最近的预期寿命下降发出了另一个令人震惊的信号。 了解美国人的总体健康状况如何与饮食相关的危险因素。 影响人们的健康并找到有效的方法来帮助他们改善生活习惯 不幸的是，现实世界中的饮食评估是公共卫生中最重要的任务之一。 设置极其复杂并且实施起来不准确。 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 可以制定有效的干预措施来控制肥胖和相关慢性疾病。 提出一个生物医学工程项目来解决饮食评估问题， 利用先进的数学模型、可穿戴电子设备和人工 智力。 十多年来，我们的研究团队一直在提高评估饮食的能力。 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、被动地收集基于图像的饮食数据（即不依赖于受试者的饮食数据） 我们还开发了计算算法来计算。 自从 eButton 开发以来，它已被广泛使用。 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 成人和儿童。 尽管过去取得了成功，但仍然存在两个挥之不去的关键问题 使用可穿戴设备进行客观被动饮食评估：1）大量手册 研究人员需要目视检查图像数据来识别食物并估计 它们的体积（份量），以及 2）研究人员查看的内容存在隐私问题 尽管解决这些问题可以使 eButton 和其他技术成为可能。 对于大规模饮食摄入研究的可穿戴设备，我们无法找到有效的解决方案 直到最近人工智能（AI）出现，尤其是那些先进的人工智能系统。 基于深度学习，可以通过大量标记数据训练产生结果 在许多应用领域中，与人类生产的产品相当甚至更好。 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 因此，我们建议开发一种仿人人工智能。 系统从图像中识别食物，估计份量大小，并找到能量和营养素 使用人工智能方法，无需从数据库中获取值。 供研究人员查看参与者的真实图像，AI系统充分尊重个人的 隐私，因为它经过训练只能识别人类食物，而不是其他。 目前，现有人工智能系统的性能受到广泛的多样性和多样性的限制。 人类食物的变异性很大，训练数据不足，很难找到合适的食物 在此应用程序中，我们提出了一种新策略来获取食品数据库中的营养信息。 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 通过这一个性化步骤，我们设想的人工智能的维度。 系统可以大幅减少，我们的目标是自动、客观和被动饮食 我们还建议改进电子硬件。 并开发仿生相机来扩大 eButton 的视野。 使用人类在现实环境中对个性化人工智能系统进行全面评估 科目。