TY - DATA T1 - Dataset of continuous human activities performed in arbitrary directions collected with a distributed radar network of five nodes PY - 2025/09/16 AU - Ronny G. Guendel AU - Matteo Unterhorst AU - Nicolas Kruse AU - Simin Zhu AU - Alexander Yarovoy AU - Francesco Fioranelli UR - DO - 10.4121/16691500.v6 KW - Micro-Doppler KW - Classification KW - Distributed Radar KW - Machine Learning KW - Assisted Living KW - Human Activity Recognition KW - Human Activity Recognition (HAR) N2 -

Please review the document README_v3.pdf

The data can be extracted with the MATLAB Live Script dataread.mlx, or in Python with the file dataread_numpy.py.


Update 2025-09-16 (by Simin Zhu):

The code and processed radar data used in publication [4] have been added here as supplementary material (Zip file named as the publication). Before using them, please read the provided README file. The supplement contains a separate README, and you will need to unpack the compressed folder to access the contents.


Referencing the dataset

Please click the orange "Citation" button above.

Paper references are:

[1] Guendel, R.G., Fioranelli, F.,Yarovoy, A.: Distributed radar fusion and recurrent networks for classification of continuous human activities. IET Radar Sonar Navig. 1–18 (2022). https://doi.org/10.1049/rsn2.12249


[2] R. G. Guendel, F. Fioranelli and A. Yarovoy, "Evaluation Metrics for Continuous Human Activity Classification Using Distributed Radar Networks," 2022 IEEE Radar Conference (RadarConf22), 2022, pp. 1-6, doi: 10.1109/RadarConf2248738.2022.9764181.


[3] R. G. Guendel, M. Unterhorst, E. Gambi, F. Fioranelli and A. Yarovoy, "Continuous human activity recognition for arbitrary directions with distributed radars," 2021 IEEE Radar Conference (RadarConf21), 2021, pp. 1-6, doi: 10.1109/RadarConf2147009.2021.9454972.


[4] S. Zhu, R. G. Guendel, A. Yarovoy and F. Fioranelli, "Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5115215, doi: 10.1109/TGRS.2022.3189746.


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