With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users’ contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%.

中文翻译：

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基于OGC IndoorGML的人对人和人对地方COVID-19联系人跟踪系统

随着低成本接近传感器的广泛应用，大量研究集中在使用接近传感器的数字人对人接触跟踪应用中。在大多数接触跟踪应用中，SARS-CoV-2的影响是通过在封闭场所接触受污染的表面传播而忽略的。这项研究的重点是使用开放OGC IndoorGML标准跟踪室内场所中的人类接触。本文提出了一种基于图的数据模型，该模型在分层结构中考虑了室内位置，时间和用户上下文的语义。对于具有可伸缩系统体系结构的COVID-19联系人跟踪应用程序，评估了所建议数据模型的功能。通过空间拓扑可以对室内轨迹进行预处理，以检测并删除语义上无效的现实世界轨迹点。结果表明，过滤掉了91.18％的语义无效的室内轨迹数据点。此外，室内轨迹数据分析通过从用户配置文件中提取的语义用户上下文（例如，消毒活动）得到了创新性的支持。在增强的接触者跟踪方案中，考虑到消毒活动和访问公用场所的顺序，通过将不必要的潜在接触滤除了44.98％，其效果优于接触者跟踪结果。但是，人与地方联系人跟踪的平均执行时间增加了58.3％。在增强的接触者跟踪方案中，考虑到消毒活动和访问公用场所的顺序，通过将不必要的潜在接触滤除了44.98％，其效果优于接触者跟踪结果。但是，人与地方联系人跟踪的平均执行时间增加了58.3％。在增强的接触者跟踪方案中，考虑到消毒活动和访问公用场所的顺序，通过将不必要的潜在接触滤除了44.98％，其效果优于接触者跟踪结果。但是，人与地方联系人跟踪的平均执行时间增加了58.3％。