Reconstruction of Human Trajectories Based on Anonymous Sensor Data for Estimating Infection Route

The COVID-19 Pandemic has increased the demands of governments for technologies to estimate the route of infection. In this paper, we propose a new smart city framework that collects anonymized passage information from deployed Bluetooth sensors and analyzes them to reconstruct the multiple trajectories of infected people. We formulate recovering multiple trajectories on the basis of anonymized passage information, including passage time and passage position, obtained by sensors in a smart city as a problem of multiple-trajectory reconstruction in general networks. We propose a new method for reconstructing multiple trajectories on the basis of anonymized passage information. Our method assumes that each trajectory follows a Markov process and estimates transit time for each edge in networks and the transition probability of the Markov process. On the basis of its estimation, our method can find multiple trajectories with maximum likelihood by solving a minimum cost flow problem. We evaluate the performance of our method in experiments using simulation data and actual human trajectory data. © 2022 IEEE.

Developing a Solution for Mobility and Distribution Analysis Based on Bluetooth and Artificial Intelligence.

The purpose of this research was to develop a simple, cost-effective, but enough efficient solution for locating, tracking and distribution analysis of people and/or vehicle flowing, based on non-intrusive Bluetooth sensing and selective filtering algorithms employing artificial intelligence components. The solution provides a tool for analyzing density of targets in a specific area, useful when checking contact proximities of a target along a route. The principle consists of the detection of mobile devices that use active Bluetooth connections, such as personal notebooks, smartphones, smartwatches, Bluetooth headphones, etc. to locate and track their movement in the dedicated area. For this purpose, a specific configuration of three BT sensors is used and RSSI levels compared, based on a combination of differential location estimates. The solution may also be suited for indoor localization where GPS signals are usually weak or missing; for example, in public places such as subway stations or trains, hospitals, airport terminals and so on. The applicability of this solution is estimated to be vast, ranging from travel and transport information services, route guidance, passenger flows tracking, and path recovery for persons suspected to have SARS-COV2 or other contagious viruses, serving epidemiologic enquiries. The specific configuration of Bluetooth detectors may be installed either in a fixed location, or in a public transport vehicle. A set of filters and algorithms for triangulation-based location of detected targets and movement tracking, based on artificial intelligence is employed. When applied in the public transport field, this setup can be also developed to extract additional information on traffic, such as private traffic flowing, or passenger movement patterns along the vehicle route, improved location in absence of GPS signals, etc. Field tests have been carried out for determining different aspects concerning indoor location accuracy, reliability, selection of targets and filtering. Results and possible applications are also presented in the final section of the paper.