USRP Respiratory Data

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Umer, Saeed

Description

This dataset presents human respiratory patterns collected using a Software-Defined Radio (SDR)-based Radio Frequency (RF) sensing system. The data was acquired using a USRP X310 device with VERT2450 omni-directional antennas and validated using a Vernier Go Direct respiration belt as ground truth.The dataset includes respiratory signals recorded from five healthy participants under controlled experimental conditions at three different orientation angles (45°, 90°, and 180°), simulating real-world positioning scenarios. Three breathing patterns were captured:Normal Respiration (NR)Fast Respiration (FR)Sleep Apnea Respiration (SAR)Each respiratory sample was recorded for 10 seconds. The raw Channel State Information (CSI) data was collected and subsequently preprocessed to produce structured datasets suitable for machine learning and signal processing applications.The final dataset consists of nine CSV files corresponding to different combinations of breathing patterns and angles. Each CSV file contains processed CSI data with 2000 features per sample, representing respiratory waveform characteristics.In total, the dataset includes:5 participants3 breathing patterns3 orientation angles225 experimental recordings32,850 total samples across all filesThis dataset is suitable for:Machine learning-based respiratory classificationRF sensing and wireless signal analysisHealthcare monitoring researchSignal processing studies involving human physiological signals How to Cite?If you use this dataset, please cite:U. Saeed and S. A. Shah, “Human Respiratory Data Collection Using USRP SDR Device,” Zenodo, 2026. https://doi.org/10.5281/zenodo.19040427

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.6

FAIR Score

88%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

© 2026 Umer Saeed and Syed Aziz Shah. This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. You are free to share and adapt the material for any purpose, provided appropriate credit is given to the original authors.

Assigned Domain

Subfield

Computer Networks and Communications

Field

Computer Science

Domain

Physical Sciences

Confidence Score

50%

Source

Scholar Data Model

Keywords

RF sensing, SDR, USRP X310, CSI, Respiratory Monitoring, Sleep Apnea Detection, Machine Learning, Wireless Sensing, Healthcare Monitoring

Normalization Factors

FT

51.92

CTw

1.00

MTw

1.00