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

Developments in microwave and millimeter-wave systems have enabled remote sensing
techniques traditionally used in long-range applications to be employed in the relatively …

This paper presents a framework based on multilayer bi-LSTM network (bidirectional Long
Short-Term Memory) for multimodal sensor fusion to sense and classify daily activities' …

It is an important topic to effectively perceive the gestures of the elderly and patients,
communicate conveniently with the deaf and dumb, and accurately transmit abnormal alarm …

Open-set activity recognition remains as a challenging problem because of complex activity
diversity. In previous works, extensive efforts have been paid to construct a negative set or …

Generative adversarial networks (GANs) have recently been proposed for the synthesis of
RF micro-Doppler signatures to address the issue of low sample support and enable the …

Recently, deep neural networks (DNNs) have been the subject of intense research for the
classification of radio frequency signals, such as synthetic aperture radar imagery or micro …

This paper presents different information fusion approaches to classify human gait patterns
and falls in a radar sensors network. The human gaits classified in this work are both …

In remote sensing, micro-Doppler signatures are widely used in moving target detection and
automatic target recognition. However, since Doppler signatures are easily affected by the …

This paper addresses the problem of radar-based human gait recognition based on the dual-
channel deep convolutional neural network (DC-DCNN). To enrich the limited radar data set …