Non-contact, imaging photoplethysmography (IPPG) uses video sequence to measure variations in light absorption, caused by blood volume pulsations, to extract cardiopulmonary parameters including heart rate (HR), pulse rate variability, and respiration rate. Previous researches most focused on extraction of these vital signs base on the focus video, which require a static and focusing environment. However, little has been reported about the influence of defocus blur on IPPG signal’s extraction. In this research, we established an IPPG optical model in defocusing motion conditions. It was found that the IPPG signal is not sensitive to defocus blur by analysis the light intensity distribution in the defocus images. In this paper, a real-time measurement of heart rate in defocus and motion conditions based on IPPG was proposed. Automatically select and track the region of interest (ROI) by constructing facial coordinates through facial key points detection, obtained the IPPG signal. The signal is de-noised to obtain the spectrum by the wavelet filtering, color-distortion filter (CDF) and fast Fourier transform (FFT). The peak of the spectrum is corresponded to heartbeats. Experimental results on a data set of 30 subjects show that the physiological parameters include heart rate and pulse wave, derived from the defocus images captured by the IPPG system, exhibit characteristics comparable to conventional the blood volume pulse (BVP) sensor. Contrast experiment show that the difference between the results measured by both methods is within 3 beat per minute (BPM). This technology has significant potential for advancing personal health care and telemedicine in motion situation.
In the field of biomedical monitoring, Image Photoplethysmography (IPPG) enables contactless monitoring of resting heart rate (HR). However, while people are in motion such as head rotation, walking back and forth, or jogging in situ, the measurement accuracy of HR is susceptive to motion-induced signal distortion. In addition, in the scene of multi-people, how to accurately distinguish each person’s signal in real time is a critical issue. In this paper, a robust and real-time HR measurement system for multi-people using Open Computer Vision library (OpenCV) library is proposed, which mainly consists of five parts: face detection by feature points acquirement, a novel, fast yet simple face tracking, region of interest (ROI) adaptive extraction for increasing motion tolerance, signal processing for pulse extraction, and HR calculation via fast Fourier transform (FFT) under the double threads framework. Using Bland-Altman plots and Pearson’s correlation coefficient (CC), the HR estimated from videos recorded by a color CCD camera is compared to a figure blood volume pulse (BVP) senor to analyze agreement. The experiment results on 28 subjects show that the max average absolute error of HR estimation is less than 5 beats per minute (BPM), and that the CC is 0.910. In our case, the frame rate is 25 frames per second (FPS) for concomitant measurement of 7 subjects with a resolution of 1024×768 pixels. Overall, our HR measurement system for multi-people meets the requirements of accuracy, motion robustness, and real-time performance, and better extends the application range of IPPG technology.