Novel Approaches for Predicting Unstructured Short Periods of Physical Activities in Youth

 描述（由申请人提供）：在过去十年中，使用加速度计测量青年体力活动的情况大幅增加。加速度计通常佩戴在腰部，但最近替代放置部位（例如手腕）变得更加流行。例如，国家健康和营养检查调查（NHANES）目前正在使用手腕位置进行他们的加速度计身体活动测量。目前，缺乏关于使用ActiGraph加速度计分析手腕位置的加速度计数据的最佳方法的信息。此外，加速度计技术的进步允许利用机器学习算法的原始加速度信号，该算法能够预测活动并改进先前使用的方法的能量消耗估计。该研究小组的成员已经证明，与人工神经网络和支持向量机等常用方法相比，使用Bipart可以改善青少年的活动分类和能量消耗预测。然而，这项初步工作是基于加速度计计数数据和结构化的实验室活动，只有一个髋关节磨损加速度计。此外，还有一些限制， 将基于实验室的模型应用于真正的自由生活活动。例如，自由生活活动不是在结构化的回合中进行的，而是在一天的过程中在微回合中进行的，这对于设计用于在指定时间段内查看一串数据的方法来说是有问题的。目前，在对活动类型进行分类和预测能量消耗之前，如何首先对自由生活活动进行分段的工作很少。该提案将扩展我们以前的工作，使用更先进的方法来分析原始加速度数据（80 Hz），使用单个手腕或臀部佩戴加速度计。100名青年将在半结构化模拟自由生活期间（发展组）进行测量，200名青年将在课后计划和家中（验证组）进行真正的自由生活活动。测量将包括能量消耗的间接量热法和活动类型的直接观察。该研究的具体目的是：1）开发和验证机器学习算法，以使用以下各项对青年自由生活活动期间的活动回合进行分段：A）髋关节佩戴式加速度计或B）腕关节佩戴式加速度计，以及2）开发和验证机器学习算法，以使用以下各项对青年自由生活活动期间的身体活动类型进行分类并估计能量消耗：A）臀部佩戴的加速计或B）手腕佩戴的加速计。这些研究的结果将对使用加速度计的身体活动研究人员产生直接和直接的影响，并通过提供准确和精确的方法来进行回合分割，预测活动类型和能量消耗来分析NHANES手腕加速度计数据。

 DESCRIPTION (provided by applicant): The use of accelerometers for the measurement of physical activity in youth has increased substantially over the last decade. Accelerometers are typically worn on the waist, however recently alternative placement sites such as the wrist have become more popular. For example, the National Health and Nutrition Examination Survey (NHANES) is currently using the wrist location for their accelerometer physical activity measurements. Currently there is a lack of information about best approaches for analyzing accelerometer data from the wrist location with the ActiGraph accelerometer. In addition, advances in accelerometer technology allow for utilization of the raw acceleration signal with machine learning algorithms, which have the capability to predict activities and improve the estimates of energy expenditure over previously used methods. Members of this research group have shown improved activity classification and energy expenditure prediction in youth using Bipart, versus commonly used approaches such as Artificial Neural Networks and Support Vector Machine. However, this preliminary work was based on accelerometer count data and structured lab activities with only a hip worn accelerometer. In addition, there are limitations to applying lab based models to true free-living activity. For example, free-living activity is not performed in structured bouts rather it is performed in micro-bouts over the course of the day which is problematic for methods designed to look at a string of data over a specified time period. There is minimal work currently on how to first segment bouts of free-living activity before classifying activity type and predicting energy expenditure. This proposal will extend our previous work using more advanced methods to analyze raw acceleration data (80 Hz) using a single wrist or hip worn accelerometer. One hundred youth will be measured during a semi-structured simulated free-living period (development group) and 200 youth will be measured during true free-living activity during an after-school program and at home (validation group). Measurements will include indirect calorimetry for energy expenditure and direct observation for activity type. The specific aims of the study are to: 1) develop and validate machine learning algorithms to segment bouts of activity during free-living activity in youth using: A) a hip worn accelerometer or B) a wrist worn accelerometer, and 2) develop and validate machine learning algorithms to classify physical activity type and estimate energy expenditure in youth, during free-living activity using: A) a hip worn accelerometer or B) a wrist worn accelerometer. Results from these studies will have direct and immediate impact for physical activity researchers utilizing accelerometers as well as analysis of NHANES wrist accelerometer data by providing ac- curate and precise methods for bout segmentation, prediction of activity type and energy expenditure.