Implementing Hierarchical Indoor Semantic Location Identity Classification: A Case Study for COVID-19 Proximity Tracking in the Philippines

Abstract

Efforts toward COVID-19 proximity tracking in closed environments focus on efficient proximity identification by combining it with indoor localization theory for location activity monitoring and proximity detection. But these are met with concerns based on existing considerations of the localization theory like costly infrastructure, multi-story support, and over-reliance on sensor networks. Semantic location identities (SLI), or location data stored with additional meaningful context, has become a feasible localizing factor especially in locations that have multiple spaces with different usage from each other. There is also a novel method of classification framework, called hierarchical classification, that leverages the hierarchical structure of the labels to reduce model complexity. The research aims to provide a solution to proximity analysis and location activity monitoring considering guidelines released in a Philippine context that addresses concerns of indoor localization and handling of geospatial data by implementing a hybrid hierarchical indoor semantic location identity classification that focuses on observable events within context-unique locations.