Abstract
Heart rate measurement using a continuous wave Doppler radar sensor (CW-DRS) has been applied to cases where non-contact detection is required, such as the monitoring of vital signs in home healthcare. However, as a CW-DRS measures the speed of movement of the chest surface, which comprises cardiac and respiratory signals by body motion, extracting cardiac information from the superimposed signal is difficult. Therefore, it is challenging to extract cardiac information from superimposed signals. Herein, we propose a novel method based on a matched filter to solve this problem. The method comprises two processes: adaptive generation of a template via singular value decomposition of a trajectory matrix formed from the measurement signals, and reconstruction by convolution of the generated template and measurement signals. The method is validated using a dataset obtained in two different experiments, i.e., experiments involving supine and seated subject postures. Absolute errors in heart rate and standard deviation of heartbeat interval with references were calculated as and for the lying posture, and and for the sitting posture.
Highlights
Cardiopulmonary activity-related information, such as heart rate (HR) and respiratory rate, is known to be an effective indicator for monitoring the health and mental state of a subject
CW-Doppler radar sensor (CW-DRS) have been investigated for application in situations where contact with the subject is hazardous for the experimenter or when restraining the subject is difficult; examples include screening systems for infectious diseases at airports to prevent the spread of infectious diseases [10,11,12,13], health monitoring of patients with burns or skin lacerations during disasters [14,15], and biometric monitoring while driving [16]
The demodulated signal was forwarded to the bandpass filter (BPF) stage, which is the pre-processing for the heartbeat enhancement by matched filtering and the heartbeat template generation stage in the matched filtering
Summary
Cardiopulmonary activity-related information, such as heart rate (HR) and respiratory rate, is known to be an effective indicator for monitoring the health and mental state of a subject. Saluja et al [25] used a gamma filter as a machine-learning approach to remove the harmonics of breathing These methods were developed to eliminate the harmonics of respiration and do not consider body motion. We propose SVD+MF method that combines an MF and singular value decomposition (SVD) as a signal processing algorithm to detect heart rate information with high accuracy from chest surface vibrations of short data length (less than 30 s). In this method, a heartbeat signal recovery technique involving an MF is used, and singular vectors are applied in the trajectory matrix of the measured signal as templates. We evaluated the effectiveness of our method in various situations by conducting measurements in two different postures: lying and sitting
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