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Abstract
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%.
Highlights
Some significant differences between our study and their study are: (1) their proposed data model is focused on museum visitors, while we proposed a general spatiotemporal graph-based model which can be used in COVID-19 contact tracing application; (2) spatial granularity was supported in the proposed model by Kontarinis et al [24], the user contextual information was overlooked; (3) temporal and contextual granularity will be supported in our proposed model to fully represent the spatiotemporal nature of users’ movement trajectories; (4) our proposed data model is implemented in a graph-based database instead of using a thematic representation of indoor trajectories
This paper introduces a graph-based semantic indoor trajectory data model that can be utilized in different indoor trajectory analyses
The OGC IndoorGML standard and its multi-layer space model are incorporated in the proposed data model for the semantic segmentation of raw indoor movement trajectories and hierarchical representation of cell spaces in a building (i.e., Bluetooth Low Energy (BLE) beacon coverage, rooms, category of rooms, floors, and buildings)
Summary
The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Depending on the size of SARS-CoV-2-laden droplets, they can either rapidly fall out of the air in the immediate environment around the infected host (i.e., cause contamination on surfaces close to the emission point) or remain suspended in the air and travel over tens of meters [7]. The spread of COVID-19 can occur directly by being in direct contact with an infected person or indirectly through touching contaminated surfaces. Amongst the different strategies used to decrease the infection rate of COVID-19, contact tracing is utilized as a public health practice [2,9].
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