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

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.

Citation

Yang, S., Kernec, J. L., Romain, O., Fioranelli, F., Cadart, P., Fix, J., …Jin, T. (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), 1813-1824. https://doi.org/10.1109/jbhi.2023.3240895

• 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
• DOI: https://doi.org/10.1109/jbhi.2023.3240895
• Keywords: Human activity classification, radar, machine learning, convolutional neural networks