Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar

TL;DR

This paper introduces a radar-based system integrated into a semi-autonomous rescue robot for detecting and localizing multiple persons and estimating their vital signs in challenging disaster environments, enhancing search and rescue capabilities.

Contribution

The study presents a complete signal processing pipeline for multi-person detection, localization, and vital sign estimation using a semi-autonomous robot with a through-wall radar, validated on a new challenging dataset.

Findings

Effective multi-person detection and localization in complex scenarios

Accurate breathing frequency estimation from radar signals

Public availability of dataset and code for further research

Abstract

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included. The full emergency response benchmark data set as well as all codes to reproduce our results, are publicly available at https://doi.org/10.21227/4bzd-jm32.