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

• Published in: IEEE journal of biomedical and health informatics Vol. PP; no. 4; pp. 1 - 13
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-04-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Angular position; Angular velocity; Benchmark testing; Classification; Classification algorithms; convolutional neural networks; Datasets; Engineering Sciences; Evaluation; Feature extraction; Home environment; Human activity classification; machine learning; Monitoring; Moving targets; Radar; Radar imaging; Radar signatures; Target detection; Training; radar; convolutional neural networks; machine learning; Human activity classification radar machine learning convolutional neural networks; Human activity classification
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2023.3240895

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.