UWB radar dataset for victim detection through foliage in Search and Rescue operations.
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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 it wearing a respiration belt [4] for breath recording. On the same tripod two anemometers [2],[3] where placed so a comprehesive image of the weather condiditon during the session could be obtained. All these sensors were connected to a laptop via USB, about 3 meters away. The distance between the tripod and the obstacle was fixed at 1 meter. Each 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]
项目描述 西雅典大学（University of West Attica, UniWA）的研究团队针对搜救行动中透过植被检测遇险人员的问题展开了攻关。为此，我们依托自研工具采集了野外环境下的呼吸信号会话数据集，该工具由超宽带（UWB）脉冲雷达系统构成；随后将采集得到的数据输入机器学习工具，通过提供植被后方人员存在的预测结果，优化野外救援（FR）作业。此外，我们采用两台风速计（anemometer）采集气象数据，并使用呼吸带（respiration belt）获取受试者呼吸速率的地面实况测量值。 所有会话的采集设置均保持一致：超宽带脉冲雷达安装于三脚架上，对准待测植被；受试者佩戴呼吸带躲在植被后方以采集呼吸数据。同一三脚架上还安装了两台风速计[2][3]，以便全面获取会话期间的气象条件。所有传感器通过USB接口连接至约3米外的笔记本电脑。三脚架与障碍物的固定距离为1米。每处植被（多为灌木丛与小型橄榄树）的长度在1至3米之间，受试者（存在目标会话中）与植被的距离为0.5至3米。整体测试范围未超过雷达的9.2米无模糊量程限制。 数据集说明 本数据集包含268组雷达、风速与呼吸带数据会话，其中141组对应存在人员目标场景，127组对应无人员目标场景。每组会话的时长为150秒，因此人员存在场景的数据总时长约为6小时，无人员场景的数据总时长约为5.5小时。 数据集内容 每个会话文件夹均以posixtime（Unix时间戳）格式的时间戳作为唯一命名X，该名称代表会话启动的精确时刻。例如在数据集预览中可见名为"1688457913"的文件夹，其对应于posixtime格式时间戳1688457913启动的测量会话（即本示例中X=1688457913）。此外，以posixtime命名的X文件夹包含如下子文件夹与文件： 1. 名为Workspaces_X的子文件夹，内含： - 若干名为"Workspace_k.mat"的文件，其中k为工作区编号，各文件存储了16 FPS帧率下采集的雷达信号记录； - 名为"settings.mat"的文件，内含设备设置与本次会话的拓扑距离参数； - 名为"windData_original.mat"的文件，内含风速传感器的原始数据：数据由遵循RS485协议的微控制器采集，以4 FPS帧率从数据流中保存。 2. 两个仅针对存在人员目标且使用呼吸带获取受试者呼吸速率地面实况的会话文件夹，包含呼吸带记录的原始数据： - 文件"1_YY_MM_DD_HH_MM_general.csv"：内含传感器与安卓设备的datetime格式时间戳、心率估算值、平均每分钟呼吸次数，以及集成的惯性测量单元（IMU）腰带传感器测量数据； - 文件"1_YY_MM_DD_HH_MM_wave.csv"：内含传感器与安卓设备的datetime格式时间戳，以及呼吸带应变片传感器在18 FPS帧率下的位移变化数据。 3. 名为"X.xlsx"的文件：内含拼接后的雷达信号工作区数据。 4. 名为"windData_X.csv"的文件：内含与雷达数据同步后的风速传感器数据。 5. 名为"BeltWfm_X.xlsx"的文件：内含与雷达数据同步后的呼吸带数据（仅针对存在人员目标且使用呼吸带获取受试者呼吸速率地面实况的会话文件夹）。 自研工具 本研究所用工具的商用现货（COTS）组件包括： - SLMX4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（UART TTL转RS485转换器、MT3608直流/直流转换器、Arduino开发板） - 呼吸带[4]
