Recognition of human movements with radar for ambient activity monitoring is a developed
area of research that yet presents outstanding challenges to address. In real environments …

In this paper, we propose a Multi-View Convolutional Neural Network and Long Short-Term
Memory (CNN-LSTM) network which fuses multiple “views” of the time-range-Doppler radar …

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 …

The accurate classification of activity patterns based on radar signatures is still an open
problem and is a key to detect anomalous behavior for security and health applications. This …

This paper presents some preliminary results to develop a generalized system for human
activity recognition (HAR) and detecting fall events using micro-Doppler signatures …

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

Recently, radar-based human activity recognition (HAR) has attracted the attention of
researchers as it has been proven that a deep learning (DL) model can be automatically …

Human activity recognition and deep learning are two fields that have attracted attention in
recent years. The former due to its relevance in many application domains, such as ambient …

Radar-based human activity recognition (HAR) has great potential in many fields, such as
surveillance, smart homes, and human-computer interaction. Complex deep neural …

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' …