FusIon Data Tracking System (FITS)

The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on global positioning systems (GPS) or similar systems, primarily targeted at outdoor use cases. However, the indoor tracking capabilities of current solutions either lack precision or are available in closed buildings only. The literature proposes sensor fusion approaches, but many of those are based on specific sensors. These approaches do not reveal implementation details or data to allow for their independent evaluation. Therefore, this article presents FusIon Data Tracking System (FITS) as an approach and proof-of-concept to facilitate the correlation of data from different indoor sensors to movement profiles of different individuals. Functionally, FITS does this by generating synthetic sensor measurement data based on real-world movement data and correlating objects tracked from distinct sensors by effectively solving clustering and position prediction tasks. This correlation is evaluated based on different metrics [multiple object tracker accuracy/precision (MOTA/MOTP)] in four different scenarios, for example, sparse data, high density of sensors, low density of sensors, and a base case. Finally, FITS's performance was evaluated by increasing the load test (dataset up to 100000 measurements and 1000 visitors) to assess whether near real-time processing is feasible under a high workload.

Automated summary

This article presents a method called FusIon Data Tracking System (FITS) as an approach and proof-of-concept to correlate data from different indoor sensors to movement profiles of different individuals. FITS achieves this by generating synthetic sensor measurement data and effectively solving clustering and position prediction tasks, improving the accuracy and effectiveness of indoor tracking.