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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.

摘要相当大比例的户外体育活动是在人行道/街道上进行的。例如，我们发现，在前一周步行的成年人中，约有70%使用了他们家周围的人行道/街道。在地域一级采取的干预措施（例如，社区）和研究之间的关系环境条件（例如，交通）和步行/骑自行车，需要可靠的身体活动测量在人行道/街道上表演。块行走法（BWM）是比较常用的方法之一可用于此目的。虽然它利用可靠的观察技术，并显示标准的有效性，自2006年推出以来，相对保持不变。它是一种非技术性的、劳动密集型的、首先生成方法推进BWM将大大有助于我们对物理的理解活动行为因此，拟议研究的目的是开发和测试一种新的压载水处理，可穿戴视频设备（WVD）和计算机视频分析，以评估在人行道/街道。为实现这一目标，将完成以下目标。目标1：改善通过将WVD纳入方法学中来实现压载水管理。WVD是一副高清晰度的眼镜摄像机嵌入到框架中。我们期望WVD是改善使用压载水管理收集的数据的获取和准确性。目标2：通过以下方式推进WVD增强的BWM 应用机器学习和识别软件自动提取身体活动信息发生在视频中的人行道/街道上。方法：经过培训的观察员（一人佩戴，一人未佩戴佩戴WVD）将以设定的步速沿着预定的沿着1000英尺一起行走。人行道/街道段代表低、中和高步行区域。在行走过程中，非WVD观察者将使用传统的BWM记录站/坐、步行、骑自行车和沿着跑步的人数，片段WVD观察者只会在行走时录制视频。稍后，两名调查人员将查看视频，以确定沿所述段沿着进行身体活动的个体的数量。对于目标2，视频数据将使用多个深度卷积神经网络（CNN）自动分析，确定一个区段中的人的数量以及正在进行的体力活动的类型。平淡 Altman方法和组内相关系数将用于评估一致性。的潜在来源诸如遮挡的误差（例如，树木）将使用主持人分析进行评估。我们希望新的方法将提高测量精度，同时减轻数据收集的负担。今后，我们将扩大 WVD-CNN系统的能力，以允许确定捕获的其他特征，例如热量消耗和环境条件的视频。我们的长期目标是，改善对身体活动的评估和我们对身体活动行为的理解。