A cutting edge approach to assessing physical activities occurring on sidewalks/streets

评估人行道/街道上发生的身体活动的前沿方法

基本信息

批准号：
9755242
负责人：
Gregory Mitchell Dominick
金额：
$ 19.32万
依托单位：
UNIVERSITY OF DELAWARE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9755242
关键词：
Address Adult Affect Age Agreement Area Auditory Behavior Canada Characteristics Child Chronic Disease Communities Complex Computer software Computers Consumption Data Data Collection Devices Diabetes Mellitus Energy Metabolism Enhancement Technology Environmental Risk Factor Error Sources Expenditure Eyeglasses Funding Agency Future Generations Geography Goals Home environment Human Hypertension Individual Intervention Knowledge Los Angeles Machine Learning Malignant Neoplasms Measurement Measures Methodology Methods New York City Obesity Outcome Patient Self-Report Physical activity Physical assessment Play Process Psyche structure Public Health Research Research Personnel Resources Role Running Social Environment System Techniques Technology Testing Time Training Transportation Trees Walking advanced system convolutional neural network disorder risk experience falls follow-up healthy lifestyle hypercholesterolemia improved innovation interest national surveillance novel strategies physical inactivity physical science practical application success technology development walkability

项目摘要

Abstract A considerable proportion of outdoor physical activity is done on sidewalk/streets. For example, we found that ~70% of adults who walked during the previous week used the sidewalks/streets around their homes. Interventions conducted at geographical levels (e.g., community) and studies examining relationships between environmental conditions (e.g., traffic) and walking/biking, necessitate a reliable measure of physical activities performed on sidewalks/streets. The Block Walk Method (BWM) is one of the more common approaches available for this purpose. Although it utilizes reliable observation techniques and displays criterion validity, it remains relatively unchanged since its introduction in 2006. It is a non-technical, labor-intensive, first generation method. Advancing the BWM would contribute significantly to our understanding of physical activity behavior. Therefore, the objective of the proposed study is to develop and test a new BWM that utilizes a wearable video device (WVD) and computer video analysis to assess physical activities performed on sidewalks/streets. The following aims will be completed to accomplish this objective. Aim 1: Improve the BWM by incorporating a WVD into the methodology. The WVD is a pair of eyeglasses with a high definition video camera embedded into the frames. We expect the WVD to be a viable option for improving the acquisition and accuracy of data collected using the BWM. Aim 2: Advance the WVD-enhanced BWM by applying machine learning and recognition software to automatically extract information on physical activities occurring on the sidewalks/streets from the videos. Methods: Trained observers (one wearing and one not wearing the WVD) will walk together at a set pace along predetermined, 1000 ft. sidewalk/street segments representing low, medium, and high walkable areas. During the walks, the non-WVD observer will use the traditional BWM to record the number of individuals standing/sitting, walking, biking, and running along the segments. The WVD observer will only record a video while walking. Later, two investigators will view the videos to determine the numbers of individuals performing physical activities along the segments. For aim 2, the video data will be analyzed automatically using multiple deep convolutional neural networks (CNNs) to determine the number of humans in a segment as well as the type of physical activities being performed. Bland Altman methods and intraclass correlation coefficients will be used to assess agreement. Potential sources of error such as occlusions (e.g., trees) will be assessed using moderator analyses. We expect the new approach will enhance measurement accuracy while reducing the burden of data collection. In the future, we will expand the capabilities of the WVD-CNNs system to allow for the determination of other characteristics captured by the videos such as caloric expenditure and environmental conditions. Our long-term goal is to substantially improve the assessment of physical activity and our understanding of physical activity behavior.

抽象的在人行道/街道上进行的户外体育锻炼很大一部分。例如，我们发现在上一周走路的成年人中，约有70％使用房屋周围的人行道/街道。在地理层面（例如社区）和研究研究之间的研究之间进行的干预措施环境条件（例如，交通）和步行/骑自行车需要可靠的体育锻炼来衡量在人行道/街道上表演。块步行方法（BWM）是最常见的方法之一可用于此目的。尽管它利用可靠的观察技术并显示标准有效性，但自2006年推出以来，它一直保持相对不变。它是一个非技术，劳动密集型，首先生成方法。推进BWM将对我们对身体的理解做出重大贡献活动行为。因此，拟议的研究的目的是开发和测试一个利用的新BWM 可穿戴视频设备（WVD）和计算机视频分析，以评估进行的体育活动人行道/街道。以下目标将完成以实现这一目标。目标1：改善 BWM通过将WVD纳入方法。 WVD是一对具有高清的眼镜嵌入到框架中的摄像机。我们希望WVD是改进的可行选择使用BWM收集的数据的获取和准确性。目标2：推进WVD增强的BWM 应用机器学习和识别软件自动提取有关体育活动的信息发生在视频中的人行道/街道上。方法：训练有素的观察者（一个穿着，一个不穿穿着WVD）将以预定的1000英尺/人行道/街道的预定速度一起行走代表低，中和高步行区。在步行期间，非WVD观察者将使用传统的BWM记录站立/坐着，散步，骑自行车和沿着的个人数量细分市场。 WVD观察者只会在步行时录制视频。后来，两名调查人员会看到视频以确定在细分市场沿线进行体育活动的个人数量。对于目标2，视频数据将使用多个深层卷积神经网络（CNN）自动分析确定一个细分市场中的人数以及正在进行的体育活动的类型。平淡 Altman方法和类内相关系数将用于评估一致性。潜在的来源将使用主持人分析评估诸如遮挡（例如树木）之类的错误。我们期望新方法将提高测量精度，同时减少数据收集的负担。将来，我们将扩展 WVD-CNNS系统的功能允许确定其他特征诸如热量支出和环境条件之类的视频。我们的长期目标是实质上改善对体育活动的评估以及我们对体育活动行为的理解。