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The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Yang, Shufan; Kernec, Julien Le; Romain, Olivier; Fioranelli, Francesco; Cadart, Pierre; Fix, Jeremy; Ren, Chenfang; Manfredi, Giovanni; Letertre, Thierry; Saenz, Israel David Hinostroza; Zhang, Jifa; Liang, Huaiyuan; Wang, Xiangrong; Li, Gang; Chen, Zhaoxi; Liu, Kang; Chen, Xiaolong; Li, Jiefang; Wu, Xing; Chen, Yichang; Jin, Tian


Julien Le Kernec

Olivier Romain

Francesco Fioranelli

Pierre Cadart

Jeremy Fix

Chenfang Ren

Giovanni Manfredi

Thierry Letertre

Israel David Hinostroza Saenz

Jifa Zhang

Huaiyuan Liang

Xiangrong Wang

Gang Li

Zhaoxi Chen

Kang Liu

Xiaolong Chen

Jiefang Li

Xing Wu

Yichang Chen

Tian Jin


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.

Journal Article Type Article
Acceptance Date Jan 23, 2023
Online Publication Date Jan 30, 2023
Publication Date 2023-04
Deposit Date Jan 31, 2023
Publicly Available Date Jan 31, 2023
Journal IEEE Journal of Biomedical and Health Informatics
Print ISSN 2168-2194
Electronic ISSN 2168-2208
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 27
Issue 4
Pages 1813-1824
Keywords Human activity classification, radar, machine learning, convolutional neural networks


The Human Activity Radar Challenge: Benchmarking Based On The ‘Radar Signatures Of Human Activities’ Dataset From Glasgow University (accepted version) (1.8 Mb)

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