In this paper, we consider semi-supervised training of an attention-augmented convolutional
autoencoder (AACAE) for human activity recognition using radar micro-Doppler signatures …

In this paper, we consider human activity recognition with a semi-supervised convolutional
autoencoder (CAE), augmented by an attention mechanism, using radar micro-Doppler …

We propose an attention-augmented convolutional autoencoder for human activity
recognition using radar micro-Doppler signatures. We use attention to overcome the limited …

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 …

Radar-based human activity recognition (HAR) offers a non-contact technique with privacy
protection and lighting robustness for many advanced applications. Complex deep neural …

Short-range radars are widely used for micro-Doppler-based human activity recognition by
using the super-vised training paradigm. However, acquiring labeled trained RF data is not …

Identifying human activities using short-range and low-power radars has attracted much
attention among the researchers and consumer electronics industry. This paper considers …

Deep learning has shown tremendous prospects in human activity recognition based on
micro-Doppler radar. However, the deficiency of radar data because of the extremely high …

Wireless sensing and human activity recognition is an important research direction for next-
generation wire-less communications systems as they evolve towards Integrated Sensing …

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 …