Multimodal Geolocation and Traffic Management in Healthcare Centers

Throughout the COVID-19 pandemic, there has been an emerging need of using new advances in information and communication technologies (ICT) to improve our healthcare system to manage a massive influx of patients. Indoor geolocation techniques and optimized traffic management algorithms are substantial for serving a large number of patients in healthcare centers while respecting procedures and protocols implemented during the pandemic. In this project, we will develop a framework which combines the signal from multiple communications techniques to precisely locate the position of users in a center. We will also apply multiple artificial intelligence (AI) and machine learning (ML) techniques to improve the geolocation and filter interferences. Then, we will assess the risk of COVID-19 infection of people living in a building or a healthcare facility. The framework will be implemented on Nano Data Center (NDC) edge computing platform of Humanitas’s to perform real-time assessment. The outcome of this project will contribute significantly to reopening our society and economy in post-pandemic era.

Faculty Supervisor:

Kim Khoa Nguyen

Student:

Partner:

Humanitas Solutions

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

University:

École de technologie supérieure

Program: