A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using an RGB Camera

Hamideh Ghanadian, Mohammad Ghodratigohar, Hussein Al Osman
2018 IEEE Access  
Recording and monitoring vital signs is an essential aspect of home-based healthcare. Using contact sensors to record physiological signals can cause discomfort to patients, especially after prolonged use. Hence, remote physiological measurement approaches have attracted considerable attention as they do not require physical contact with the patient's skin. Several studies proposed techniques to measure Heart Rate (HR) and Heart Rate Variability (HRV) by detecting the Blood Volume Pulse (BVP)
more » ... om human facial video recordings while the subject is in a resting condition. In this thesis, we focus on the measurement of HR. We adopt an algorithm that uses the Independent Component Analysis (ICA) to separate the source (physiological) signal from noise in the RGB channels of a facial video. We generalize existing methods to support subject movement during video recording. When a subject is moving, the face may be turned away from the camera. We utilize multiple cameras to enable the algorithm to monitor the vital sign continuously, even if the subject leaves the frame or turns away from a subset of the system's cameras. Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. We systematically test our method on eleven subjects using four cameras. The proposed method decreases the RMSE by 27% compared to the state of the art in the rest condition. When the subject is in motion, the proposed method achieves a RMSE of 1.12 bpm. iii Acknowledgments I would like to extend my greatest appreciation to Dr. Hussein Al Osman for his tremendous support and encouragement he has shown during the last two years. He has always listened to me patiently and enthusiastically and guided me toward the best solutions by asking deep questions. He taught me how to do academic research. He has read my drafts carefully, and has generously provided me with detailed feedback on my research paper and on my thesis. He has been honest enough to point out my weaknesses to me and to push me to work harder, and caring and supportive enough to see my potential even when I could not. I am extremely grateful to my mother, Sedigheh, and my father, Ahmad, for believing in me, even when I didn't believe in myself, for always encouraging me to be the best person I can be. They have been pillars of support, guidance and love in my life since the day I was born. I cannot express how important their presence has been throughout my life. I also thank my brothers, Ali, Hossein and Abolfazl for being part of my foundation, for all of the support they've provided me over the last several years, and for all of the incredible strength they've forced me to see in myself.
doi:10.1109/access.2018.2872756 fatcat:42ofwq7bp5dspeuk3oaeyvyn5e