A deep learning based heart rate estimation method for millimeter wave radar

Abstract

Contact-based vital sign detection technology has been widely used in the medical field. However, contact-based devices may cause discomfort to users and suffer from user dependency issues. Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar provides an efficient and accurate solution for heart rate and respiratory rate monitoring. Nevertheless, due to the small amplitude of heartbeat micro-motion signals, they are susceptible to noise interference such as respiratory harmonics, making accurate measurement challenging. Aiming at the problem that traditional methods are difficult to adapt to different environmental noises, we propose a heart rate estimation method based on a convolutional neural network. The heart rate estimation accuracy is significantly improved by recognizing the phase change patterns in radar signals. We reduce the interference of respiratory harmonics on the heart rate micromotion signal by decomposing the extracted phase signal into multiple frequency components using the Empirical Wavelet Transform (EWT) algorithm. The proposed deep learning model is used by means of depth convolution in order to balance the model size and accuracy. Additionally, we introduce time–frequency channels into the convolutional neural network to further enhance its feature extraction capability. Comparisons with various related works on heart rate distribution, sensing range, and subjects demonstrate the lightweight nature and higher accuracy of the proposed model. Extensive experiments are conducted on a dataset based on the TI AWR1642BOOST radar. The results show that the proposed method achieves outstanding performance, with an accuracy of $\pm 5$ BPM in heart rate monitoring.