Motion Resistant Facial Video based Heart Rate Estimation Method using Head-Mounted Camera

Abstract

Imaging photoplethysmography (iPPG) technique has a bright prospect in noncontact heart rate (HR) and other types of vital signs detection; however, the iPPG method is highly susceptible to the motion artifacts in blood volume pulse (BVP) detected from facial video, and this is crucial for HR estimation. Although a number of signal processing algorithms regarding the resistance to motion interference have been proposed in recent years, their stability and computational complexity are hard to meet the requirements of practical applications. To address this problem, we suggest using the head-mounted camera (HMC) instead of a fixed position camera (FPC) in iPPG method because the HMC can move synchronously with the head, which can effectively alleviate the impact of motion artifacts. To this end, a prototype of HMC was designed and implemented. By referring to the results of CMS50E pulse oximeter synchronized with the subject's videos, the comparison study of HMC and FPC in iPPG-based HR estimation was conducted in three scenarios of resting, talking and head rotation. Furthermore, a HMC-based iPPG database named IPPG-HMC database was built. The experimental results show that HMC-based iPPG methods can deal with motion artifacts efficiently, and significantly improve the reliability and accuracy of HR estimation.