Passive and Privacy-preserving Human Localization via mmWave Access Points for Social Distancing

TL;DR

This paper presents a passive, privacy-preserving human localization method using mmWave access points to monitor social distancing with high accuracy, leveraging wireless signals and machine learning.

Contribution

It introduces a novel mmWave-based localization approach that is passive, privacy-preserving, and cost-effective for social distancing enforcement.

Findings

Achieves about 99% localization accuracy in tested scenarios.

Utilizes directive mmWave transmissions for passive human detection.

Provides a privacy-preserving alternative to traditional localization methods.

Abstract

The pandemic outbreak has profoundly changed our life, especially our social habits and communication behaviors. While this dramatic shock has heavily impacted human interaction rules, novel localization techniques are emerging to help society in complying with new policies, such as social distancing. Wireless sensing and machine learning are well suited to alleviate viruses propagation in a privacy-preserving manner. However, its wide deployment requires cost-effective installation and operational solutions. In public environments, individual localization information-such as social distancing-needs to be monitored to avoid safety threats when not properly observed. To this end, the high penetration of wireless devices can be exploited to continuously analyze-and-learn the propagation environment, thereby passively detecting breaches and triggering alerts if required. In this paper, we describe a novel passive and privacy-preserving human localization solution that relies on the directive transmission properties of mmWave communications to monitor social distancing and notify people in the area in case of violations. Thus, addressing the social distancing challenge in a privacy-preserving and cost-efficient manner. Our solution provides an overall accuracy of about 99% in the tested scenarios.