Motion Artifact Reduction in PPG Signals from Face: Face Tracking & Stochastic State Space Modeling Approach

Abstract

The Photoplethymography(PPG) is generally measured on a finger or an ear using contact sensors. The recent several studies using non-contact sensor such as CCD camera and web-cam to measure PPG have been introduced under the desktop or mobile computing environment. However the motion artifact issue is also emerging in non-contact camera sensing similar to contact-type one because it is sensitive to artifacts generated by subject’s head and body motion. In this paper, the two sequential approaches for a motion artifact reduction algorithm are presented; the one is a face tracking method that detects and tracks the skin region of face which is containing PPG signals, the other is the reduction method of motion artifact due to various head & face movement such as roll, yaw, pitch, translation and scale. PPG signals are modeled by stochastic state space modeling(SSM) approach and its system parameters are estimated by subspace system identification. Finally, the Kalman filter(KF) built by these parameters is applied to predict and correct distorted PPG signals. Results of the proposed KF are compared to these of the FIR band pass filter(BPF).