Human State Recognition Using Ultra Wideband Radar Based on CvT

Emotions have a significant impact on an individual’s life, and positive emotions can enhance their quality of life. Moreover, emotions play a crucial role in the field of medicine. However, the complexity of human emotions has hindered their rapid development in the medical field. Therefore, it is crucial to rapidly and accurately identify human emotions. In the field of emotion recognition, radar is widely used for monitoring human physiological features due to its strong penetrability and noncontact advantages. In deep learning (DL), the Transformer model has been introduced into radar signal processing due to its excellent global perception ability, offering new possibilities for fast and accurate detection of human emotions. In this article, we propose a human emotion recognition system based on ultrawideband radar, utilizing the convolutional vision transformer (CvT) model as the DL model for emotion classification. The CvT model incorporates convolution into the Vision Transformer architecture, combining the advantages of both in image recognition tasks. Unlike previous works with convolutional networks, CvT fully leverages the benefits of convolution while retaining the characteristics of the Transformer. We pretrained the model on a publicly available radar data set and then conducted experimental validation using our collected data set. The experimental results demonstrate that our network outperforms traditional convolutional approaches, achieving a test accuracy of 86.25%, which is of significant importance for radar-based emotion recognition.