The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

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 conditions 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.

A holistic human activity recognition optimisation using AI techniques

“…The activities were chosen to be challenging from the perspective of recognition in the context of assisted living. Activities 4 and 5 are particularly difficult to recognise from one another [5].…”

“…The binarised image, called 'mask', can be used for feature extraction. The mask can also be applied to the other MDS representations to acquire a 'masked' MDS domain as Equation (5).…”

“…In assisted living, radar sensors are being developed commercially by companies, such as Vayyar, Xandar Kardian. Furthermore, it is actively researched in academia [4,5].…”

“…It is widely utilised in radarbased HAR since it provides salient features to distinguish activities [6]. Considerable research in HAR focuses on MDS, that is, the amplitude of MDS, also known as the spectrogram, using both hand-crafted features and deep learning models [1,4,5,7].…”

Radar‐based human activity recognition using denoising techniques to enhance classification accuracy

“…Relevant research compares the performance of different radar data domains in classification tasks, including raw complex radar data, range-Doppler, range-time, Cadence Velocity Diagrams (CVD), and Cepstrogram [5,[8][9][10]. Different domains provide different advantages to the classification task.…”