KEYWORDS: Electrocardiography, Radar signal processing, Heart, Education and training, Radar, Vital signs, Signal processing, Signal detection, Reflection
With the increasing application of radar in non-contact vital signs monitoring, researchers have extensively explored methods to extract respiratory and heartbeat information from radar echoes to assess human health. However, current research primarily focuses on accurately estimating respiration and heart rates, which cannot meet the need of comprehensive health monitoring. Additionally, medical professionals do not directly analyze patient conditions through radar signals, but with electrocardiogram (ECG) signals. Thus, this paper aims to explore the mapping relationship between radar echoes and ECG signals to further reveal the vital sign information contained therein. A deep learning model based on complex-valued neural networks and self-attention mechanism is proposed to realize the conversion of radar signals and corresponding ECG signals. The experiment result shows that the proposed method can successfully convert radar vital signs into ECG signals, with an average Pearson correlation coefficient of 0.98.
In this paper, the radar parameters of human body junction including different behavioral physical characteristics are analyzed. The radar parameters of different activities (walking, squatting, sitting and falling) of the target under multiple observation angles are simulated. The radar characteristics of human motion such as Doppler frequency, radial distance, height and pitch angle are analyzed in depth. Finally, the most suitable view angle in the classification of each activity and the radar parameters with the most stable characteristics are further given. The feature extraction method presented in this paper has an explicit physical interpretation and good angular adaptability. Neural networks combined with the proposed feature extraction method can have better interpretation of the activity classification results.
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