Yang, S. et al. (2023) The Human Activity Radar Challenge: benchmarking based on the ’Radar signatures of human activities’ dataset from Glasgow University. IEEE Journal of Biomedical and Health Informatics, 27(4), pp. 1813-1824. (doi: 10.1109/JBHI.2023.3240895)
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Abstract
Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.
| Item Type: | Articles |
|---|---|
| Additional Information: | The authors would like to thank the British Council (grant 515095884, 514739586), Campus France 44764WK–PHC Alliance France-UK, the UK EPSRC (grant INSHEP EP/R041679/1), and the Dutch Research Council NWO (grant RAD-ART), for their financial support. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Fioranelli, Dr Francesco and Yang, Dr Shufan and Romain, Professor Olivier and Le Kernec, Dr Julien |
| Authors: | Yang, S., Le Kernec, J., Romain, O., Fioranelli, F., Cadart, P., Fix, J., Ren, C., Manfredi, G., Letertre, T., Hinostroza Saenz, I. D., Zhang, J., Liang, H., Wang, X., Li, G., Chen, Z., Liu, K., Chen, X., Li, J., Wu, X., Chen, Y., and Jin, T. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Journal Name: | IEEE Journal of Biomedical and Health Informatics |
| Publisher: | IEEE |
| ISSN: | 2168-2194 |
| ISSN (Online): | 2168-2208 |
| Published Online: | 30 January 2023 |
| Copyright Holders: | Copyright © 2023 IEEE |
| First Published: | First published in IEEE Journal of Biomedical and Health Informatics 27(4): 1813-1824 |
| Publisher Policy: | Reproduced with the permission of the Publisher |
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