Towards indoor localisation analytics for modelling flows of movements

Indoor localisation has been an active area of research for the last decades, and while substantial research aims to increase localisation accuracy, little has been done in developing localisation data analytics for indoor spaces. There is a wide range of scenarios and applications in which efficiency is of the essence and localisation data could be used to optimise the general flow of people. For instance, Hospitals' Operating Rooms (ORs) cost up to $1,5001 per hour even when not being used, and therefore improving staff and patients' flow to maximise OR utilisation is important. By using indoor localisation and a long-term deployment to identify delays and timeliness in the steps that lead to a surgery, the hospital can better schedule surgeries to increase the up-time of ORs. Likewise, moving heavy assets through multi-floored construction sites can result in injuries and high costs. Minimising these movements by studying the flow of workers and assets can potentially result in a safer and healthier working environment and in a lower overall costs. Museums, zoos, festivals, and other exhibit-based sites can benefit from a more streamlined deployment and analysis of people's flow and insights on historical data. As of now, the process to turn indoor localisation data to useful analytics is not straightforward, remains bespoke, and costly.
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