Automated Ecological Video Identification of Physical Activity (E-VIP) Software

 描述（由申请人提供）：本提案通过在常见环境中推进人群PA评估，解决了PAR-12-197“改善饮食和体力活动（PA）评估”的多个优先事项。缺乏身体活动每年导致美国20万人死亡，全球500万人死亡。大约10%的乳腺癌和10%的结肠癌可归因于PA不足，不活动导致9%的过早死亡。公园、学校和青少年体育活动是与大多数人口相关的公共艺术的重要贡献者。在这些环境中支持巴勒斯坦权力机构将有助于提高巴勒斯坦权力机构的人口水平和疾病预防与控制。我们将开发一个新的视频分析软件系统，命名为E-VIP（PA的生态视频识别），它将估计PA的人数和聚集量从视频记录的PA相关设置。E-VIP将提供自动化和持续（而不是瞬时）的评估，这将允许不断的反馈，为适应性干预和决策提供信息。E-VIP基于计算机科学方法，用于计算人群和识别行为。我们将在来自2个公园、2所学校和2个青少年体育场所的32，400秒视频上训练E-VIP机器学习算法。参与者将参加各种活动，同时佩戴加速度计以捕获代谢当量（MET）中的PA强度。测试阶段包括在4个公园、4所学校和4个青少年体育场所拍摄900个5分钟的自由生活行为视频，其中一半的场所/区域与训练阶段相同。在培训和测试阶段将捕获一系列活动和人员密度 以最大限度地覆盖更困难的高密度情况。将对每个时间样本进行系统的直接观察，以提供检验有效性的标准测量。将测试的指标包括平均人数、每个活动类别（久坐、轻度、中度、剧烈、非常剧烈）中的平均人数比例以及5分钟（或任何给定）时间段内设置的PA MET分钟总和。将探讨性别和年龄组分类法。本研究将以体育专家授课与任课教师授课的体育课为比较对象，检验E-VIP的结构效度。将使用Bland Altman方法和组内相关系数评估一致性。潜在的误差源，如闭塞（例如，树木、阴影、其他人）将使用主持人分析进行评估。通过自动化生态PA评估，E-VIP将可广泛使用。E-VIP的连续评估能力将通过收集比现有直接观测工具更高分辨率的数据来提高精度。当通过常用的安全摄像头或网络摄像头嵌入特定设置时，或者通过有目的地放置录像机，E-VIP将能够进行持续评估，这将为公共卫生监督，干预和循证决策提供信息（例如，优化干预战略，监测学校体育任务，提供环境改造前的需求评估和环境改造后的评估）。

 DESCRIPTION (provided by applicant): This proposal addresses multiple priorities of PAR-12-197 "Improving Diet and Physical Activity (PA) Assessment" by advancing assessment of population PA in common settings. Physical inactivity is responsible for ≈200,000 deaths in the US and 5 million deaths worldwide annually. About 10% of breast and 10% of colon cancers are attributable to insufficient PA, and inactivity causes 9% of total premature mortality. Parks, schools and youth sports are important contributors of PA that are relevant to a majority of the population. Supporting PA in these settings would contribute to improvements in population levels of PA and disease prevention and control. We will develop a novel video analysis software system, named E-VIP (Ecological Video Identification of PA), which will estimate the number of people and aggregated volume of PA from video recordings of PA- relevant settings. E-VIP will provide automated and continuous (rather than momentary) assessment, which will allow ongoing feedback to inform adaptive interventions and decision making. E-VIP is based on computer science methods used to count crowds and identify behaviors. We will train the E-VIP machine learning algorithm on 32,400 seconds of video from 2 parks, 2 schools, and 2 youth sports settings. Participants will engage in a variety of activities while wearing accelerometers to capture PA intensity in Metabolic Equivalents (METs). The testing phase involves capturing 900, 5-minute videos of free-living behavior across 4 parks, 4 schools, and 4 youth sports settings, with half of the settings/zones being the same as from the training phase. A range of activities and density of people will be captured in both the training and testing phase to maximize coverage of more-difficult high-density situations. Systematic direct observation will be conducted on each time sample to provide the criterion measure for testing validity. The metrics that will be tested include the average number of people, average proportion of people in each activity category (sedentary, light, moderate, vigorous, very vigorous), and sum PA MET-minutes in the setting over the 5-minute (or any given) time period. A gender and age group classifier will be explored. PE classes taught by PE specialists vs classroom teachers will be compared to test construct validity of E-VIP. Bland Altman methods and intraclass correlation coefficients will be used to assess agreement. Potential sources of error such as occlusions (e.g., trees, shadows, other people) will be assessed using moderator analyses. By automating ecological PA assessment, E-VIP will be feasible for widespread use. E-VIP's capability of continuous assessment will improve precision by collecting higher resolution data than collected by existing direct observation tools. When embedded in specific settings through commonly-used security cameras or webcams, or by purposefully placing video recorders, E-VIP will be capable of ongoing assessment which will inform public health surveillance, intervention, and evidence-based decision making (e.g., optimizing intervention strategies, monitoring school Physical Education mandates, providing needs assessment prior to and evaluation after environmental modifications).