Noncontact vital signs detection using joint wavelet analysis and autocorrelation computation

Vital signs are important parameters for human health status assessment, and timely, accurate detection is of great significance for modern health care and intelligent medical applications. Detecting vital signs, such as heartbeat and respiration signals, provides a variety of diseases with reliable diagnosis and effective prevention. Conventional contact detection may restrict the behaviors of users, cause additional burdens, and render users uncomfortable. In recent years, noncontact detection technology has successfully achieved remote long-term detection for respiration and heartbeat signals. Compared to conventional contact-detection approaches, noncontact heartbeat and respiration detection using a millimeter-wave radar is preferable as it causes no disturbance to the subject, bringing a comfortable experience, and detects vital signs under natural conditions. However, noncontact vital signs detection is challenging owing to environmental noise. Especially, heartbeat signals are very weak and are merged with respiration harmonics and environmental noise, and their extraction and recognition are even more difficult. This paper applied a frequency-modulated continuous wave (FMCW) radar to detect vital signs. The study also presented a noncontact heartbeat and respiration signals detection approach based on wavelet analysis and autocorrelation computation (WAAC). The millimeter-wave FMCW radar first transmited the electromagnetic signal and received the reflected echo signals from the human body. Thereafter, the phase information of the intermediate frequency signals was extracted, which included respiration and heartbeat signals. The direct current offset of the phase information was corrected, and the phase was unwrapped. Finally, the wavelet packet decomposition was used to reconstruct heartbeat and respiration signals from the original signal, and an autocorrelation computation was utilized to reduce the effect of clutters on the heart rate detection. Experiments were conducted on ten subjects. Results show that the average absolute error percentage of WAAC is less than 1.65% and 1.83% for respiration and heartbeat rates, respectively.