A Motion Robust Remote-PPG Approach to Driver's Health State Monitoring

Abstract

With the surging significance of personal health care, driver's physiological state is no longer negligible nowadays. Among all the indicators of health state in human, heart rate (HR) is one of the most cardinal indicators. The commonly used HR measurement is contact-type, might result in driver's distraction and discomfort in the vehicle applications. To cope with this problem, remote photoplethysmography (rPPG) is utilized to monitor HR at a distance via a web camera. Nevertheless, the rPPG is not without its flaw. The main concern of the rPPG technique is the potential not-robustness result from the arbitrary motion. Consequently, the contribution of this paper is to conquer the motion noise when the car is driving and the driver's health state is well monitored to enhance the public safety. The proposed algorithm is investigated in not only the indoor environment but as well the outdoor driving, which contains much more unpredictable motion. With k-nearest neighbor (kNN) classifier on chrominance-based features, the mean square error can be reduced from 30.6 to 2.79 bpm, approaching the medical instrument level. The proposed method can be applied to human improving driving safety for Advanced Driver Assistance Systems.