Contact-free measurement of heartbeat signal via a Doppler radar using adaptive filtering
ABSTRACT It has been proven that the cardiopulmonary signs, including respiration and heartbeat signal, can be contact-free measured via a Doppler radar. However, the heartbeat signal cannot be identified when the human subject does not hold his or her breath. To resolve the problem, the adaptive noise canceller (ANC) based on recursive-least square (RLS) algorithm is presented to simultaneously measure the heartbeat and the respiration signal. Experimental results showed that not only can the heartbeat signal be well identified, but the heart rate also strongly correlated with that derived from the electrocardiogram (ECG).
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- "Non-contact measurements of physiological parameters can be achieved using thermal infrared imaging, a technology employed by Pavlidis et al. to collect physiological data on human faces like heart and respiratory rates, perspiration, supraorbital and periorbital blood flow    . Similarly, Doppler radars are non-contact sensors that were used to detect heartbeats   and respiration signals . More recently, digital cameras and webcams were employed on the face to detect Blood Volume Pulse (BVP)      and compute HR and Breathing Rate (BR). "
ABSTRACT: Photoplethysmographic signals obtained from a webcam are analyzed through a continuous wavelet transform to assess the instantaneous heart rate. The measurements are performed on human faces. Robust image and signal processing are introduced to overcome drawbacks induced by light and motion artifacts. In addition, the respiration signal is recovered using the heart rate series by respiratory sinus arrhythmia, the natural variation in heart rate driven by the respiration. The presented algorithms are implemented on a mid-range computer and the overall method works in real-time. The performance of the proposed heart and breathing rates assessment method was evaluated using approved contact probes on a set of 12 healthy subjects. Results show high degrees of correlation between physiological measurements even in the presence of motion. This paper provides a motion-tolerant method that remotely measures the instantaneous heart and breathing rates. These parameters are particularly used in telemedicine and affective computing, where the heart rate variability analysis can provide an index of the autonomic nervous system.Biomedical Signal Processing and Control 11/2013; 8(6):568–574. DOI:10.1016/j.bspc.2013.05.010 · 1.53 Impact Factor
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ABSTRACT: This paper reviews recent advances in technologies using Doppler radar to detect heartbeat and respiration of a human subject. With contributions from many researchers in this field, new detection methods and system architectures have been proposed to improve the detection accuracy and robustness. The advantage of noncontact/covert detection has drawn interests on various applications. While many of the reported systems are bench-top prototypes for concept demonstration, several portable systems and integrated radar chips have been demonstrated. This paper reviews different architectures and discusses their potentials for integrated circuit implementation. Integrating the radar sensor on a chip allows the function of noncontact vital sign and vibration detection to be embedded in portable electronic equipment, like many other radio frequency (RF) devices. A radar sensor network is then feasible for pervasive monitoring in healthcare applications.
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ABSTRACT: Remote heart rate detection without body-attached probes is a promising technology for health care, monitoring of elderly people, emergency, and security. In this paper, we use a continuous wave (CW) microwave Doppler radar. It is important to eliminate the effect of body movement that is irrelevant to heartbeat such as respiration. In general, the displacements of them are larger than those of heartbeat. Therefore, we focus on the periodic variation of velocity of body movement due to heartbeat rather than the displacement variation of it. We detect a heart rate from a part of the wavelet frequency components with high periodicity. As a result of performance evaluation, our system enables to extract more accurate heartbeat interval than the traditional approach using the periodicity of an original Doppler signal.Sensors Applications Symposium (SAS), 2011 IEEE, San Antonio, TX; 02/2011