Real-time heart rate measurement for multi-people using compressive tracking

Lingling Liu; Yuejin Zhao; Ming Liu; Lingqin Kong; Liquan Dong; Feilong Ma; Zongguang Pang; Zhi Cai; Yachu Zhang; Peng Hua; Ruifeng Yuan

doi:10.1117/12.2272644

19 September 2017 Real-time heart rate measurement for multi-people using compressive tracking

Lingling Liu, Yuejin Zhao, Ming Liu, Lingqin Kong, Liquan Dong, Feilong Ma, Zongguang Pang, Zhi Cai, Yachu Zhang, Peng Hua, Ruifeng Yuan

Author Affiliations +

Proceedings Volume 10396, Applications of Digital Image Processing XL; 1039621 (2017) https://doi.org/10.1117/12.2272644
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

The rise of aging population has created a demand for inexpensive, unobtrusive, automated health care solutions. Image PhotoPlethysmoGraphy(IPPG) aids in the development of these solutions by allowing for the extraction of physiological signals from video data. However, the main deficiencies of the recent IPPG methods are non-automated, non-real-time and susceptible to motion artifacts(MA). In this paper, a real-time heart rate(HR) detection method for multiple subjects simultaneously was proposed and realized using the open computer vision(openCV) library, which consists of getting multiple subjects’ facial video automatically through a Webcam, detecting the region of interest (ROI) in the video, reducing the false detection rate by our improved Adaboost algorithm, reducing the MA by our improved compress tracking(CT) algorithm, wavelet noise-suppression algorithm for denoising and multi-threads for higher detection speed. For comparison, HR was measured simultaneously using a medical pulse oximetry device for every subject during all sessions. Experimental results on a data set of 30 subjects show that the max average absolute error of heart rate estimation is less than 8 beats per minute (BPM), and the processing speed of every frame has almost reached real-time: the experiments with video recordings of ten subjects under the condition of the pixel resolution of 600× 800 pixels show that the average HR detection time of 10 subjects was about 17 frames per second (fps).

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Lingling Liu, Yuejin Zhao, Ming Liu, Lingqin Kong, Liquan Dong, Feilong Ma, Zongguang Pang, Zhi Cai, Yachu Zhang, Peng Hua, and Ruifeng Yuan "Real-time heart rate measurement for multi-people using compressive tracking", Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039621 (19 September 2017); https://doi.org/10.1117/12.2272644

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