DOI: 10.5281/zenodo.8214497
title: Dataset on the Human Body as a Signal Propagation Medium

description: <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> info@edi.lv