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

 描述(申请人提供)：在过去的十年中，使用加速度计测量青年的体力活动有了很大的增加。加速计通常戴在腰部，但最近手腕等替代位置变得更受欢迎。例如，国家健康和营养检查调查(NHANES)目前正在使用手腕位置来测量加速度计的体力活动。目前，缺乏关于使用Actgraph加速度计分析腕部位置的加速度计数据的最佳方法的信息。此外，加速计技术的进步使机器学习算法能够利用原始的加速度信号，这些算法能够预测活动并改进对能源消耗的估计，而不是以前使用的方法。该研究小组的成员表明，与人工神经网络和支持向量机等常用方法相比，使用BiPart对年轻人的活动分类和能量消耗预测有所改善。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，只使用髋部佩戴的加速度计。此外，还存在以下限制 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，而是在一天的过程中以微回合的形式进行，这对于设计用于查看特定时间段内的一串数据的方法来说是有问题的。目前，关于如何在对活动类型进行分类和预测能量消耗之前，首先对自由生活活动进行细分的工作很少。这项建议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赫兹)，使用单个手腕或臀部穿戴的加速度计。100名青年将在半结构化模拟自由生活期间被测量(发展组)，200名青年将在课后和在家的真正自由生活活动期间被测量(验证组)。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。这项研究的具体目的是：1)开发和验证机器学习算法，以使用：A)髋部佩戴的加速度计或B)手腕佩戴的加速度计来分割青年在自由生活活动期间的活动回合，以及2)开发和验证机器学习算法，以分类青少年在自由生活活动期间的身体活动类型并估计能量消耗，使用：A)髋部佩戴的加速度计或B)手腕佩戴的加速度计。这些研究的结果将对使用加速度计的体力活动研究人员以及对NHANES腕部加速度计数据的分析产生直接和即时的影响，因为它为比赛分段、活动类型预测和能量消耗提供了准确和精确的方法。

 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.