Almost all existing methods for human activity classification based on micro-Doppler radar
first manually convert the raw radar signal into a spectrogram using a short time Fourier …

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 …

Previously, the 2-D convolutional neural networks (2-D-CNNs) have been introduced to
classify the human activity based on micro-Doppler radar. Whereas these methods can …

We propose a method for radar-based human activity classification by a neural network
taking both micro-Doppler and range signatures of the target as input. Temporally localized …

Radar-based human activity recognition has been of great interest due to its capability to
resolve problems of the security and health system. Deep learning-based methods are …

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 …

Human detection and activity classification has recently become a key technology in many
applications, eg, human computer interaction and surveillance for public and industrial …

A convolutional neural network (CNN) based deep learning (DL) approach to classify
human activities in micro-Doppler spectrogram of radar is investigated. MOCAP dataset …

Radar-based activity recognition is a problem that has been of great interest due to
applications such as border control and security, pedestrian identification for automotive …

The movements of the human body and limbs result in unique micro‐Doppler signatures,
which can be exploited for classifying human activities. In this work, the authors propose a …