Calculation of Percent Body Fat by Analyzing Virtual Body Models

 描述(由申请人提供)：过多的体脂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。美国医学会最近宣布，肥胖本身就是一种疾病。大多数流行病学研究利用身体质量指数(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更直接地与身体脂肪过多有关，而不是与体重有关，因此测量身体脂肪对于更准确的指南至关重要。然而，准确评估身体脂肪的方法既昂贵又不方便，而且需要固定的设备。因此，美国医学会呼吁采用更具成本效益和更方便的方法来评估身体成分，以帮助医生进行评估和治疗。尤其是人体的虚拟建模提供了扫描和分析身体及其运动的方法。有监督机器学习(SML)是人工智能的一个子领域，在获取测量数据来推断新的关系方面取得了很大的进展。我们相信，虚拟建模和SML可以提供必要的技术来方便和准确地计算体脂百分比(%BF)，并为根据体型治疗肥胖提供新的工具。该项目将开发一种系统，该系统使用市场上可以买到的深度相机，如Microsoft Kinect(R)来捕捉人体表面。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身体脂肪百分比。该系统将通过收集若干受试者的数据进行培训和验证。捕捉到的表面将被用来探索视觉身体表征在动机和坚持中的作用。所开发的系统可以在临床或个人环境中实施，并可用作公共卫生研究工具，并在所需硬件成本较低的情况下广泛部署。除了该系统将对控制肥胖症产生立竿见影的影响外，该项目还将对许多领域产生广泛影响。随着时间的推移，捕捉到的这类体型的大型数据库可能会导致预测一个人的体型在特定干预下将如何变化的方法。某些导致身体形态变化的医疗条件，如涉及淋巴循环的情况，可能更容易诊断和跟踪。儿童的生长模式可以通过体型的变化来跟踪。可以利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。

 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.