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

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，~70%的人使用住宅周围的人行道/街道。 在地理层面(如社区)进行的干预措施和研究之间的关系 环境条件(例如交通)和步行/骑自行车，需要可靠的体力活动测量 在人行道/街道上表演。块走法(BWM)是比较常见的方法之一 可用于此目的。尽管它使用了可靠的观察技术并显示了判据的有效性，但它 自2006年推出以来，相对保持不变。这是一种非技术性、劳动密集型的 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，拟议研究的目标是开发和测试一种新的BWM，它利用 可穿戴式视频设备(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 BWM通过将WVD合并到方法学中。WVD是一副高清眼镜 摄像机被嵌入到框架中。我们期望西九龙发展计划是一个可行的方案，以改善 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：训练有素的观察员(一名佩戴，一名未佩戴 佩戴着WVD)将以设定的速度一起沿着预定的1000英尺行走。人行道/街道段 代表低、中、高可步行区域。在漫步期间，非WVD观察者将使用 传统的BWM记录站立/坐着、步行、骑自行车和沿 细分市场。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，以确定沿分段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度卷积神经网络(CNN)进行自动分析 确定一个细分市场中的人类数量以及正在进行的身体活动类型。平淡无奇 将使用Altman方法和组内相关系数来评估一致性。潜在的来源 将使用主持人分析来评估诸如遮挡(例如，树木)等错误。我们期待着新的方法 将提高测量精度，同时减轻数据收集的负担。在未来，我们将扩大 WVD-CNNS系统的能力，允许确定由 这些视频包括卡路里消耗和环境条件。我们的长期目标是大幅提高 提高对体育活动的评价和对体育活动行为的理解。

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.