Multipath Exploitation for Human Activity Recognition using a Radar Network

In this study, the problem of multipath in radar sensor networks for human activity recognition (HAR) has been examined. Traditionally considered as a source of additional clutter, the multipath is being investigated for its potential to be exploited through the creation of virtual radar nodes. These virtual nodes are conceptualized to...

A Survey on Radar-Based Continuous Human Activity Recognition

Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more....

Radar Sensing in Healthcare: Challenges and Achievements in Human Activity Classification & Vital Signs Monitoring

Driven by its contactless sensing capabilities and the lack of optical images being recorded, radar technology has been recently investigated in the context of human healthcare. This includes a broad range of applications, such as human activity classification, fall detection, gait and mobility analysis, and monitoring of vital signs such as...

Continuous Human Activity Classification with Radar Point Clouds and Point Transformer Networks

Due to numerous benefits, radar is considered as an important sensor for human activity classification. The problem of classifying continuous sequences of activities of unconstrained duration has been studied in this work. To tackle this challenge, a radar data processing method utilizing point transformer networks has been proposed. The method...

Exploiting Radar Data Domains for Classification with Spatially Distributed Nodes

Recognition of continuous human activities is investigated in unconstrained movement directions using multiple spatially distributed radar nodes, where activities can occur at unfavourable aspect angles or occluded perspectives when using a single node. Furthermore, such networks are favourable not only for the aforementioned aim, but also for...

Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability

Cognitive radar frameworks rely on the ability to quantify and reason on future uncertainty, which allows for the selection of an optimal decision policy. These methods require that the uncertainty estimates provided by the underlying statistical model are well-calibrated, i.e. consistent with true uncertainty. In this work, the utilization of...