Motion and Illumination Resistant Facial Video based Heart Rate Estimation Method using Levenberg-Marquardt Algorithm Optimized Undercomplete Independent Component Analysis

Abstract

<div>Heart Rate (HR) estimation is of utmost need due to its applicability in diverse fields. Conventional methods for HR estimation require skin contact and are not suitable for scenarios such as sensitive skin or prolonged unobtrusive HR monitoring. Therefore remote photoplethysmography (rPPG) methods have been an active area of research. These methods utilize the facial videos acquired using a camera followed by extracting the Blood Volume Pulse (BVP) signal for heart rate calculation. The existing rPPG methods either used a single color channel or weighted color differences, which has limitations dealing with motion and illumination artifacts. This study considers BVP extraction as an undercomplete problem and proposed a method U-LMA. First, a non-linear Cumulative Density Function (CDF) approximated by a hyperbolic tangent (tanh) was used to deal with the non-linearity associated with rigid and non-rigid motions and illumination variations. Then, the entropy of the proposed non-linear CDF was optimized using a customized LMA for BVP signal extraction, followed by maximum peak estimation for HR calculation. The performance of the proposed method was tested under three scenarios: constrained, motion, and illumination variations scenarios. High Pearson correlation coefficient values and smaller lower-upper statistical limits of bland-altman plots , justified the good performance of the U-LMA. Comparative analysis of U-LMA with undercomplete ICA with negentropy (U-neg) and other state-of-the-art methods demonstrated its best performance of U-LMA by achieving the lowest error and highest correlation values (0.01 significance level) . Additionally, higher accuracy satisfying the clinically accepted error differences also justified its clinical relevance.</div>