Unconstrained human activities recognition with a radar network is considered. A hybrid
classifier combining both convolutional neural networks (CNNs) and recurrent neural …

Radar systems are increasingly being employed in healthcare applications for human
activity recognition due to their advantages in terms of privacy, contactless sensing, and …

In recent years, deep convolutional neural networks (DCNNs) have demonstrated the
prominent performance in the radar-based human activity recognition. However, collecting …

Radio-frequency-based noncooperative monitoring of humans has numerous applications
ranging from law enforcement to ubiquitous sensing applications such as ambient assisted …

Building on previous radar‐based human activity recognition (HAR), we expand the micro‐
Doppler signature to 6 domains and exploit each domain with a set of handcrafted features …

Space target classification based on micro-motion characteristics has become a subject of
great interest in the field of radar, particularly when using deep learning techniques …

Millimetre‐wave radar has been widely used in health monitoring and human activity
recognition owing to its improved range resolution and operation in a variety of …

Human Activity Classification with radar has made significant progress in the past few years.
In this article, we propose a cyclostationarity-based approach in this field of application …

Radar is now widely used in human activity classification because of its contactless sensing
capabilities, robustness to light conditions and privacy preservation compared to plain …

In a time characterized by rapid technological advancements and a noticeable trend towards
an older average population, the need for automated systems to monitor movements and …