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Calculation of Percent Body Fat by Analyzing Virtual Body Models

通过分析虚拟身体模型计算身体脂肪百分比

基本信息

批准号：
9099872
负责人：
JAMES K HAHN
金额：
$ 19.43万
依托单位：
GEORGE WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-07-01 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9099872
关键词：
Adherence Age Air Algorithms Area Artificial Intelligence Behavior Therapy Belief Body Composition Body Size Body Surface Body Weight decreased Body fat Body mass index Child Chronic Disease Client Clinical Clinical Research Data Databases Development Diagnosis Disease Epidemiologic Studies Equipment Fatty acid glycerol esters Future Goals Growth Guidelines Health Health Care Costs Heart Diseases Human Human body Imagery Incentives Individual Intervention Lead Link Machine Learning Malignant Neoplasms Maps Measurement Measures Medical Methods Modeling Monitor Motion Motivation Muscle Non-Insulin-Dependent Diabetes Mellitus Obesity Outcome Overweight Participant Patients Pattern Perception Plethysmography Public Health Reporting Research Risk Role Scanning Self Perception Series Shapes Stroke Surface System Techniques Technology Time Training Underweight Variant Visual Water Weight Weights and Measures base body density body volume cost cost effective data mining density disorder risk lymphatic circulation novel novel strategies obesity treatment preference public health research reconstruction sex study population tool virtual weight loss intervention

项目摘要

DESCRIPTION (provided by applicant): Excess body fat is a key underlying factor in the development of numerous chronic diseases, including type II diabetes, heart disease, stroke, and cancer. The AMA recently declared that obesity, itself, is a disease. Most epidemiologic studies utilize Body Mass Index (BMI) to classify people as underweight, normal, overweight, or obese because it is a convenient and simple method that has been shown to correlate with disease risk. Since the majority of the health risks associated with obesity are more directly linked to an overabundance of body fat than weight, measuring body fat is essential for more precise guidelines. However, accurate methods of assessing body fat are expensive, inconvenient, and require immobile equipment. Consequently, the AMA has called for more cost effective and convenient methods to assess body composition to assist doctors in their assessment and treatment. Virtual modeling of humans in particular has provided ways to scan and analyze the body and its motion. Supervised Machine Learning (SML), a sub-field of artificial intelligence, has made great progress in taking measured data to infer new relationships. It is our belief that virtual modeling and SML can provide the techniques necessary to conveniently and accurately calculate the percentage of body fat (%BF) and to provide new tools in treating obesity based on body shapes. The project will develop a system that uses commercially available depth cameras such as the Microsoft Kinect(r) to capture the surface of the human body. This will be accomplished by developing a new algorithm to perform deformable registration of several RGB-Depth views of the body. A new algorithm that uses SML will be developed to calculate percentage body fat using the surface data. The system will be trained and validated by collecting data from a number of subjects. The surface captured will be used to explore the role of visual body representation in motivation and adherence. The developed systems can be implemented in clinical or personal settings and be utilized as a public health research tool and deployed widely given the low-cost of the hardware required. In addition to the immediate impact that the system will have on managing obesity, the project will have a broad impact on a number of areas. A large database of such shapes captured over time may lead to ways to predict how an individual's body shape will change given a particular intervention. Certain medical conditions that result in body shape change, such as those involving lymphatic circulations, may be diagnosed and tracked more easily. Growth patterns of children may be tracked by change of body shapes. Further research can be conducted to determine the effect of body shape on %BF using data mining techniques.

描述（由申请人提供）：过量的身体脂肪是许多慢性疾病发展的关键潜在因素，包括II型糖尿病，心脏病，中风和癌症。美国医学协会最近宣布，肥胖本身就是一种疾病。大多数流行病学研究使用体重指数（BMI）将人分为体重不足，正常，超重或肥胖，因为它是一种方便和简单的方法，已被证明与疾病风险相关。由于与肥胖相关的大多数健康风险与过多的身体脂肪而不是体重更直接相关，因此测量身体脂肪对于更精确的指导方针至关重要。然而，评估身体脂肪的准确方法是昂贵的，不方便的，并且需要固定的设备。因此，AMA呼吁更经济有效和方便的方法来评估身体成分，以帮助医生进行评估和治疗。特别是人类的虚拟建模提供了扫描和分析身体及其运动的方法。监督机器学习（SML）是人工智能的一个子领域，在利用测量数据推断新关系方面取得了巨大进展。我们相信，虚拟建模和SML可以提供必要的技术，方便和准确地计算身体脂肪的百分比（%BF），并提供新的工具，在治疗肥胖的基础上的身体形状。该项目将开发一个系统，使用商业上可用的深度相机，如微软Kinect（r）捕捉人体表面。这将通过开发一种新的算法来实现，以执行身体的几个RGB深度视图的可变形配准。将开发一种使用SML的新算法，以使用表面数据计算体脂百分比。该系统将通过从许多受试者收集数据进行培训和验证。所捕获的表面将被用来探索视觉身体表征的动机和坚持的作用。所开发的系统可以在临床或个人环境中实施，并被用作公共卫生研究工具，并且由于所需硬件的低成本而被广泛部署。除了该系统将对管理肥胖产生直接影响外，该项目还将对许多领域产生广泛影响。随着时间的推移捕获的这种形状的大型数据库可能导致预测个体的体型在给定特定干预下将如何变化的方法。某些导致体形改变的疾病，如淋巴循环，可以更容易地诊断和跟踪。儿童的生长模式可以通过身体形状的变化来跟踪。可以使用数据挖掘技术进行进一步的研究以确定体型对%BF的影响。