Multi-Person Localization and Vital Sign Estimation Radar Dataset

Citation Author(s):
Christian A.
Schroth
Technische Universität Darmstadt
Christian
Eckrich
Technische Universität Darmstadt
Stefan
Fabian
Technische Universität Darmstadt
Oskar
von Stryk
Technische Universität Darmstadt
Abdelhak M.
Zoubir
Technische Universität Darmstadt
Michael
Muma
Technische Universität Darmstadt
Submitted by:
Christian Schroth
Last updated:
Thu, 06/15/2023 - 03:32
DOI:
10.21227/4bzd-jm32
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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 dataset provides the possibility to develop algorithms for, e.g., radar-based (through-wall) multi-person detection, localization, 3D direction-of-arrival estimation, breathing frequency estimation or heart beat estimation. The challenging dataset was 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 ethics commission of 'Technische Universität Darmstadt' (EK 30/2023, date of approval: 10. May 2023) gave their consent in recording and publication of the dataset.

Funding Agency: 
LOEWE-Zentrum emergenCITY

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