Non-contact Heart Rate Detection Based On Camera

Noncontact heart rate monitoring that enables the detection of cardiac activity cycles with a normal camera without cutaneous contact has broad application prospects for LoT,Big Data,and machine learning.This technology depends on signal extraction,especially extracting the heart rate signal from the broken information,which promotes the development and completion of the correlative theories.And the related technologies—capturing information that can be analyzed by computer from the image,locating the face or any other specific ROI region accurately,and extracting the pattern relevant with heart rate signal—are based on the pattern recognition and machine learning.Besides this work explores its application and will accelerate its practical value.The state-of-the-art methods,which have achieved many progresses in the laboratory environment,however,have the weakness on the robustness,shorted on the face shade,noise and shadow where the test environment is on the nature,and also lack robustness to the lower illuminance and the varying illuminance.This paper,based on those methods,proposes a low-rank and sparse matrix decomposition modeling,to address those issues without the ideal laboratory environment,to evaluation the heart rate.We test our modeling on two datasets and demonstrate that,this paper provided,achieve promising results.This paper builds an optical noncontact heart rate monitoring framework(lower/vary illuminance PPG,LiPPG)with the aim of improving the robustness of these illuminance situations.In LiPPG,we use the YIQ color space to represent the PPG signal.We propose a mathematical model to extract signals in the time domain and reduce the disturbance from noise.We test the LiPPG on three scenarios: stationary,facial motion,and varying illuminance,with 18 video clips from 6 participants.The experimental results show that the noncontact heart rate estimation using the LiPPG reduced the errors compared with the gold-standard method of recording from a finger using a pulse oximeter and that it is superior to existing methods.