DOI: 10.5281/zenodo.10815247
title: A UWB Radar and Machine Learning-Based Tool for Detecting Victims Through Foliage in Search and Rescue Operations

description: Project Description

During our research in University of West Attica (UniWA) we addressed the problem of victim detection through foliage in Search and Rescue operations. For this purpose, a dataset of respiration signal sessions in the field was collected using a proposed tool consiting of a UWB pulsed radar system, and then these data fed a machine learning tool to enhance FR's operations by providing predictions about human presence behind foliage. In addition, two anemometer sensors were used to record wind data, and a respiration belt was employed to obtain the ground truth measurements about the subject's respiration rate.

The setup for each session was the same. The UWB radar [1] was mounted on tripod facing the foliage, the subject was located behind the foliage wearing a respiration belt [4] for breath recording. On the same tripod two anemometers [2],[3] were placed so a comprehesive image of the wind condiditon during the session could be obtained. These sensors were connected to a laptop via USB, about 3 meters away. The distance between the tripod and the foliage was fixed at 1 meter. Foliage (mostly bushes and small olive trees) had length varying from 1 to 3 meters and the subject (in case of presence session) was from 0.5 to 3 meters away from the foliage. In total we never exceeded the 9.2 meters range (unambiguous range) limit of the radar.

Dataset Description

The dataset consists of 268 sessions of radar, wind and respiration belt data, of which 141 sessions correspond to human presence and 127 to human absence. Each session has a duration of 150 seconds, thus amounting to approximately 6 hours of data for human presence and approximately 5.5 hours of data for human absence.

Dataset Contents

Each session folder is given an individual name X = posixtime; this name designates the exact time (in posixtime format) when the session was started. For example, in the dataset preview below there can be seen one folder named "1688457913"; this folder corresponds to the measurement session that was initiated exactly on 1688457913 in posixtime format (in this example, X = 1688457913).  Furthermore, for the "X" posixtime-named folder, there are the following subfolders and files:

1. One subfolder named Workspaces_X, containing:



Files named "Workspace_k.mat", where k the number of the created workspaces containing radar signal recording at 16 FPS.

A file named "settings.mat", containing the device settings and the session's distances regarding topology.

A file named "windData_original.mat", containing the original data from anemometer sensors saved from the data stream at 4 FPS, provided from a microcontroller followed RS485 protocol.


2. Two files containing the raw data recorded from the respiration belt (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data.)



The "1_YY_MM_DD_HH_MM_general.csv", contains the timestamp in datetime of the sensor and the Android device, the heart rate estimation, the mean breaths per minute and the included IMU belt sensor measurement.

The "1_YY_MM_DD_HH_MM_wave.csv", contains the timestamp in datetime of the sensor and the Android device, and 18 values (FPS) of the strain gauge sensor changes from the respiration belt.


3. A file named "X.xlsx", containing the concatenation of the workspaces of the radar signal.

4. A file named "windData_X.csv", containing the synchronized data of anemometer sensors with radar data.

5. A file named "BeltWfm_X.xlsx", containing the synchronized data of respiration belt with radar data (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data).

Proposed Tool COTS components



SLMX4 UWB pulse radar [1]

Wind Speed [2] and Direction [3] sensors

Wind data recording equipment (UART TTL to RS485 Converter, MT3608 DC/DC converter, Arduino)

Respiration belt [4]