Passive WiFi CSI Sensing Based Machine Learning Framework for COVID-Safe Occupancy Monitoring

The COVID-19 pandemic requires social distancing to prevent transmission of the virus. Monitoring social distancing is difficult and expensive, especially in travel corridors such as elevators and commercial spaces. This paper describes a low-cost and non-intrusive method to monitor social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behaviour of CSI with a cubic SVM classifier, we count the number of people in an elevator with an accuracy of 92%, and count the occupancy of an office to 97%. As opposed to using a multi-class counting approach, this paper aggregates CSI for the occupancies below and above a COVID-Safe limit. We show that this binary classification approach to the COVID safe decision problem has similar or better accuracy outcomes with much lower computational complexity, allowing for real-world implementation on IoT embedded devices. Robustness and scalability is demonstrated through experimental validation in practical scenarios with varying occupants, different environment settings and interference from other WiFi devices.

Automated summary

This paper presents a low-cost method to monitor social distancing using Channel State Information (CSI) from passive WiFi sensing, achieving high accuracy in travel corridors like elevators and commercial spaces, and allowing for real-world implementation on IoT embedded devices.