The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar
sensors has enabled various healthcare applications, including vital sign monitoring, fall …

We propose the use of deep convolutional neural networks (DCNNs) for human detection
and activity classification based on Doppler radar. Previously, proposed schemes for these …

Many deep learning (DL) models have shown exceptional promise in radar-based human
activity recognition (HAR) area. For radar-based HAR, the raw data is generally converted …

This paper presents the use of a deep convolutional neural network (DCNN) in
distinguishing between absence of human gait and the presence of single or multiple …

Safety and security applications benefit from better situational awareness. Radar micro‐
Doppler signatures from an observed target carry information about the target's activity, and …

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of
microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and …

A combined Gaussian mixture model and hidden Markov model (HMM) is developed to
distinguish between slow moving animal and human targets using mel-cepstrum …

In recent years, Doppler radar has been used as a sensing modality for human gait
recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles …

Many deep learning models have been proposed in radar-based human activity recognition
(HAR) area. For radar-based HAR, generally, the raw radar data is first converted to a 2-D …

With the rapid development of population aging, fall detection based on the radar manifests
great application value in the field of medicine and health. Focusing on the problem of the …