UWB radar dataset for victim detection through foliage in Search and Rescue operations.

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]

项目简介
本研究依托西阿提卡大学（University of West Attica, UniWA）开展，针对搜救作业中透过植被开展遇险人员探测的难题展开攻关。为此，本研究采用自研工具采集了野外呼吸信号会话数据集，该工具由超宽带脉冲雷达（UWB pulsed radar）系统构成；随后将上述数据输入机器学习工具，通过输出植被后方人员存在与否的预测结果，辅助优化搜救作业。此外，本研究采用两台风速传感器记录风速数据，并使用呼吸带采集受试者呼吸频率的真值标注数据。

所有数据会话的采集设置保持一致：超宽带脉冲雷达[1]安装于三脚架上，朝向植被区域；受试者位于植被后方，佩戴呼吸带[4]以采集呼吸信号。同一三脚架上还安装了两台风速传感器[2][3]，以便完整获取数据会话期间的风况信息。上述传感器通过USB与约3米外的笔记本电脑相连。三脚架与植被区域的间距固定为1米。植被区域（以灌木丛与小型橄榄树为主）的纵深范围为1至3米；存在受试者的会话中，受试者与植被区域的间距为0.5至3米。所有采集数据均未超出该雷达9.2米的无模糊量程限制。

数据集说明
本数据集共包含268组雷达、风速与呼吸带采集会话数据，其中141组对应存在受试者的场景，127组对应无受试者的场景。单组会话时长为150秒，据此计算，存在受试者场景的数据总时长约为6小时，无受试者场景的数据总时长约为5.5小时。

数据集内容
每组会话的文件夹以posixtime格式的时间戳命名为X，该名称代表会话启动的精确时间（采用posixtime格式）。例如，数据集预览中的名为“1688457913”的文件夹，即对应以posixtime格式1688457913为启动时间的采集会话（本示例中X=1688457913）。

以posixtime命名的X文件夹包含以下子文件夹与文件：
1. 名为Workspaces_X的子文件夹，内含：
- 命名为“Workspace_k.mat”的文件，其中k为工作区编号，该文件存储帧率为16 FPS的雷达信号采集数据；
- 名为“settings.mat”的文件，存储设备配置参数与本次采集会话的拓扑布局距离参数；
- 名为“windData_original.mat”的文件，存储风速传感器的原始采集数据。该数据由遵循RS485通信协议的微控制器以4 FPS的帧率从数据流中保存。
2. 两组存储呼吸带原始采集数据的文件（仅存在受试者且使用呼吸带采集真值标注数据的会话文件夹包含该内容）：
- “1_YY_MM_DD_HH_MM_general.csv”：包含传感器与安卓设备的日期时间戳、心率估算值、平均每分钟呼吸次数，以及呼吸带内置惯性测量单元（IMU）的传感器测量数据；
- “1_YY_MM_DD_HH_MM_wave.csv”：包含传感器与安卓设备的日期时间戳，以及呼吸带应变片传感器的18组采样数据（采集帧率为FPS）。
3. 名为“X.xlsx”的文件，存储雷达信号采集工作区的拼接数据。
4. 名为“windData_X.csv”的文件，存储与雷达数据同步后的风速传感器采集数据。
5. 名为“BeltWfm_X.xlsx”的文件，存储与雷达数据同步后的呼吸带采集数据（仅存在受试者且使用呼吸带采集真值标注数据的会话文件夹包含该内容）。

自研工具
本研究采用的商用现货（Commercial Off-The-Shelf, COTS）组件包括：
- SLMX4型超宽带脉冲雷达[1]；
- 风速[2]与风向[3]传感器；
- 风速数据采集设备（含UART TTL转RS485转换器、MT3608型DC/DC转换器、Arduino开发板）；
- 呼吸带[4]。

搜集汇总

背景与挑战

背景概述

该数据集是用于搜救行动中通过树叶检测受害者的研究工具，包含268个会话的雷达、风速和呼吸带数据，其中141个会话对应有人存在，127个对应无人存在，总数据时长约11.5小时。数据通过超宽带脉冲雷达系统、风速传感器和呼吸带收集，旨在支持机器学习模型训练以预测人类存在。