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.

抽象的相当一部分户外体育活动是在人行道/街道上进行的。例如，我们发现上周步行的成年人中，约 70% 使用家周围的人行道/街道。在地理层面（例如社区）进行的干预措施以及检查之间关系的研究环境条件（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/街道上进行。块步行法 (BWM) 是更常见的方法之一可用于此目的。尽管它利用了可靠的观察技术并显示了标准有效性，但它自 2006 年推出以来，保持相对不变。它是一种非技术性、劳动密集型的、第一个生成方法。推进 BWM 将极大地促进我们对物理的理解活动行为。因此，拟议研究的目标是开发和测试一种利用可穿戴视频设备（WVD）和计算机视频分析来评估身体活动人行道/街道。为了实现这一目标，将完成以下目标。目标 1：改善 BWM 通过将 WVD 纳入方法中。 WVD是一副高清眼镜嵌入框架中的摄像机。我们期望 WVD 成为改善使用 BWM 收集的数据的获取和准确性。目标 2：通过以下方式推进 WVD 增强型 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：训练有素的观察者（一名佩戴，一名未佩戴）佩戴 WVD）将沿着预定的 1000 英尺人行道/街道路段以设定的速度一起行走代表低、中、高步行区域。在步行期间，非 WVD 观察者将使用传统的 BWM 记录沿着道路站立/坐着、行走、骑自行车和跑步的人数段。 WVD 观察员只会在行走时录制视频。随后，两名调查员将查看视频以确定沿路段进行体育活动的人数。对于目标 2，将使用多个深度卷积神经网络（CNN）自动分析视频数据确定某个部分的人数以及正在进行的体育活动的类型。平淡 Altman 方法和组内相关系数将用于评估一致性。潜在来源将使用调节器分析来评估遮挡（例如树木）等错误。我们期待新的方法将提高测量精度，同时减轻数据收集的负担。未来我们将扩大 WVD-CNN 系统的能力允许确定由热量消耗和环境条件等视频。我们的长期目标是大幅提高对身体活动的评估以及我们对身体活动行为的理解。