Dataset on the Human Body as a Signal Propagation Medium

<strong>Overview:</strong> This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz. <strong>Applications:</strong> The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body. <strong>Overview statistics:</strong> Number of subjects: 30 Number of transmitter locations: 6 Number of receiver locations: 6 Number of measurement frequencies: 19 Input voltage: 1 V Load resistance: 50 ohm and 1 megaohm <strong>Measurement group statistics:</strong> Height: 174.10 (7.15) Weight: 72.85 (16.26) BMI: 23.94 (4.70) Body fat %: 21.53 (7.55) Age group: 29.00 (11.25) Male/female ratio: 50% <strong>Included files:</strong> experiment_protocol_description.docx - protocol used in the experiments electrode_placement_schematic.png - schematic of placement locations electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject RawData - the full measurement results and experiment info sheets all_measurements.csv - the most important results extracted to .csv all_measurements_filtered.csv - same, but after z-score filtering all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row all_measurements_by_freq_filtered.csv - same, but after z-score filtering summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets process_json_files.py - script that creates .csv from the raw data filter_results.py - outlier removal based on z-score plot_sample_curves.py - visualization of a randomly selected measurement result subset plot_measurement_group.py - visualization of the measurement group <br> CSV file columns: subject_id - participant's random unique ID experiment_id - measurement session's number for the participant height - participant's height, cm weight - participant's weight, kg BMI - body mass index, computed from the valued above body_fat_% - body fat composition, as measured by bioimpedance scales age_group - age rounded to 10 years, e.g. 20, 30, 40 etc. male - 1 if male, 0 if female tx_point - transmitter point number rx_point - receiver point number distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths! tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually. rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually. total_fat_level - sum of rx and tx fat levels bias - constant term to simplify data analytics, always equal to 1.0 CSV file columns, frequency-specific: tx_abs_Z_... - transmitter-side impedance, as computed by the `process_json_files.py` script from the voltage drop rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance <br> <strong>Acknowledgments:</strong> The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358. <strong>References:</strong> For a more detailed information, see this article: J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023. <strong>Contact information:</strong> [e-mail]<strong>数据集概述：</strong> 本数据集为大规模人体测量数据集，采用低压交流正弦信号对志愿者受试者进行数据采集，涵盖阻抗与信号损耗两类测量数据。信号频率范围为50 kHz至20 MHz。 <strong>应用场景：</strong> 本数据集旨在支撑以人体作为信号传播介质的相关研究，可用于探究人体属性（年龄、性别、身体成分等）、测量点位以及信号频率对人体链路信号损耗的影响规律。 <strong>数据集总体统计信息：</strong> 受试者总数：30名；发射点位数量：6个；接收点位数量：6个；测量频率种类：19种；输入电压：1 V；负载阻抗：50欧姆与1兆欧姆。 <strong>受试群体统计特征：</strong> 身高：174.10（7.15）cm；体重：72.85（16.26）kg；身体质量指数（BMI）：23.94（4.70）；体脂率：21.53%（7.55%）；平均年龄：29.00（11.25）岁；男女占比各为50%。 <strong>包含文件：</strong> - experiment_protocol_description.docx：实验方案说明文档，记录本次实验采用的完整实验流程 - electrode_placement_schematic.png：电极布设点位示意图 - electrode_placement_photo.jpg：志愿者受试者的实验布设实拍图 - RawData：存储完整测量结果与实验信息表的文件夹 - all_measurements.csv：提取的核心实验结果CSV文件 - all_measurements_filtered.csv：经过z分数异常值过滤后的核心实验结果CSV文件 - all_measurements_by_freq.csv：按频率整理的核心实验结果CSV文件，每行对应单一测量频率 - all_measurements_by_freq_filtered.csv：经过z分数异常值过滤后的按频率整理的核心实验结果CSV文件 - summary_of_subjects.csv：基于实验信息表统计得到的受试者关键信息表 - process_json_files.py：用于从原始数据生成CSV文件的Python脚本 - filter_results.py：基于z分数的异常值剔除脚本 - plot_sample_curves.py：用于随机选取部分测量结果进行可视化的脚本 - plot_measurement_group.py：用于受试群体特征可视化的脚本 <strong>CSV通用字段说明：</strong> - subject_id：受试者随机生成的唯一标识符 - experiment_id：受试者单次测量会话编号 - height：受试者身高，单位：厘米（cm） - weight：受试者体重，单位：千克（kg） - BMI：基于上述身高与体重计算得到的身体质量指数 - body_fat_%：采用生物阻抗秤测量得到的体脂率 - age_group：按10年为一档划分的年龄组，例如20、30、40等 - male：性别标识，男性取值为1，女性取值为0 - tx_point：发射点位编号 - rx_point：接收点位编号 - distance：发射与接收点位间的相对距离（未基于受试者身高与肢体长度进行缩放） - tx_point_fat_level：发射点位所在位置的平均体脂含量指标（未针对个体受试者进行单独缩放） - rx_point_fat_level：接收点位所在位置的平均体脂含量指标（未针对个体受试者进行单独缩放） - total_fat_level：发射与接收点位体脂含量指标之和 - bias：用于简化数据分析的常数项，固定取值为1.0 <strong>频率专属字段说明：</strong> - tx_abs_Z_*：由`process_json_files.py`脚本基于电压降计算得到的发射端阻抗 - rx_gain_50_f_*：采用50欧姆负载阻抗时实测得到的接收机增益，单位：分贝（dB） - rx_gain_1M_f_*：采用1兆欧姆负载阻抗时实测得到的接收机增益，单位：分贝（dB） <strong>致谢：</strong> 本数据集的采集工作由拉脱维亚科学委员会资助，项目名称为“面向体域网的人体耦合通信”，项目编号：lzp-2020/1-0358。 <strong>参考文献：</strong> 如需获取更详细信息，请参阅以下论文：J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, 及 A. Elsts. 《面向人体耦合通信的人体信号传播介质数据集》，已提交至Elsevier旗下期刊《Data in Brief》，2023年。 <strong>联系方式：</strong> [e-mail]