Human Sensing via Passive Spectrum Monitoring

TL;DR

This paper introduces a passive human sensing method using PRF spectrum alterations captured by SDR, enabling accurate human authentication, localization, and activity recognition across various environments.

Contribution

It presents a novel approach leveraging PRF spectrum signatures as a biometric modality, utilizing machine learning for diverse human sensing tasks with high accuracy.

Findings

Over 95% accuracy in human sensing tasks

Localization error less than 0.8 meters

Effective across multiple environments

Abstract

Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability.