Assessment of food intake using speech-understanding technology

使用语音理解技术评估食物摄入量

• 批准号: 8488793
• 负责人: SUSAN B ROBERTS
• 金额: $ 22.04万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-20 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8488793
• 关键词: Adult; Body Weight decreased; Clinical Treatment; Code; Computer software; Custom; Data; Databases; Development; Diet; Dietary Assessment; Dietary intake; Disease; Eating; Eating Behavior; Energy Intake; Epidemic; Etiology; Food; Food Energy; Health; Individual; Intake; Language; Lead; Life; Macronutrients Nutrition; Measures; Methodology; Methods; Minority; Monitor; Names; Nutrient; Obesity; Pilot Projects; Prevalence; Prevention; Prevention approach; Public Health; Publishing; Recipe; Regulation; Reporting; Research; Research Infrastructure; Research Project Grants; Role; Self Assessment; Software Validation; Specificity; Speech; System; Techniques; Technology; Testing; Text; Time; Touch sensation; Translational Research; Validation; Weight; Weight Gain; Woman; Work; base; design; flexibility; innovation; men; multidisciplinary; novel; nutrition; obesity treatment; phrases; programs; prototype; public health relevance; research study; treatment program; web-accessible

DESCRIPTION (provided by applicant): The prevalence of obesity remains at epidemic levels in the U.S. and worldwide. Currently, research on obesity and advances in effective public health approaches to prevention and treatment of obesity are substantially hampered by the lack of accurate, low burden methods for self-assessment of food intake. This translational research project will develop a prototype multimodal program designed to become a state-of-the- art method for self-assessment of energy and nutrient intakes. The program will allow users to interact with a custom-designed spoken dialogue system to solicit sufficient information to determine quantitative amounts of specific food items, from which detailed reports of energy and nutrient intakes will be calculated using published data on food composition. We will use our previously developed WAMI (Web-accessible Multimodal Interface) framework as the underlying infrastructure. The prototype will be tested in adult men and women, who will make audio recordings of their food intake and simultaneously use the new program over 7 days. Types and amounts of foods consumed, time taken to assess daily intake, and calculated energy intake will be compared between the new method and conventional calculations based on the audio recording. The prototype is expected to justify further developmental work and creation of a program that is both more accurate and with lower user burden that existing methods. The new program is novel and highly innovative because it will include: 1) the first use of speech understanding technology as part of multimodal dialogue interaction to reduce user burden during accurate identification of consumed foods; 2) first use of natural spoken language understanding technology to further reduce user burden; and 3) additional options to enhance accuracy and reduce user burden including individualization of food composition by users. The project is very feasible because the multidisciplinary team has extensive expertise in both the development of multimodal and speech understanding software and validation of food intake methods, and has also obtained preliminary data relevant to the application.

描述（由申请人提供）：在美国和世界范围内，肥胖症的患病率仍处于流行病水平。目前，由于缺乏准确、低负担的食物摄入自我评估方法，肥胖研究和预防和治疗肥胖的有效公共卫生方法的进展受到很大阻碍。这个转化研究项目将开发一个原型多模式程序，旨在成为最先进的能量和营养摄入自我评估方法。该程序将允许用户与一个定制设计的口语对话系统进行交互，以征求足够的信息来确定特定食品的数量，并根据公布的食品成分数据计算出详细的能量和营养摄入量报告。我们将使用之前开发的WAMI （Web-accessible Multimodal Interface）框架作为底层基础设施。该原型将在成年男性和女性中进行测试，他们将对自己的食物摄入进行录音，并在7天内同时使用新程序。新方法将与基于录音的传统计算方法比较所消耗食物的种类和数量、评估每日摄入量所需的时间以及计算出的能量摄入量。该原型有望证明进一步的开发工作和创建一个比现有方法更准确、用户负担更低的程序是合理的。新项目新颖且极具创新性，因为它将包括：1)首次使用语音理解技术作为多模式对话交互的一部分，以减轻用户在准确识别消费食品时的负担；2)首次使用自然口语理解技术，进一步减轻用户负担；3)提高准确性和减轻用户负担的附加选项，包括用户对食品成分的个性化。这个项目是非常可行的，因为这个多学科团队在多模式和语音理解软件的开发以及食物摄入方法的验证方面都有广泛的专业知识，并且已经获得了与应用相关的初步数据。