Key result
The proposed U-LMA method accurately estimated heart rate from facial videos, achieving high Pearson correlation coefficients (0.92 to 0.94) with ground truth sensors across constrained, motion, and illumination variation scenarios.
Why the study?
Existing remote photoplethysmography methods for non-contact heart rate estimation have limitations in dealing with motion and illumination artifacts.
The U-LMA algorithm improves the accuracy and reliability of non-contact heart rate estimation from facial videos by effectively handling motion and illumination artifacts.
Supports non-contact HR monitoring feasibility; leaves open prospective clinical validation before adoption.
Heart Rate (HR) estimation is of utmost importance due to its applicability in diverse fields. Conventional methods for HR estimation require skin contact and are not suitable in certain scenarios such as sensitive skin or prolonged unobtrusive HR monitoring. Therefore remote photoplethysmography (rPPG) methods have become 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 utilized a single color channel or weighted color differences, which has certain limitations dealing with motion and illumination artifacts. This study considered BVP extraction as an undercomplete problem and proposed a method resistant to motion and illumination variation artifacts. This method is based on an undercomplete independent component analysis, aiming to estimate the unmixing matrix using a non-linear Cumulative Density Function (CDF) that has been optimized using the customized Levenberg-Marquardt algorithm. Therefore, the method is named U-LMA. 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 outstanding performance of the proposed U-LMA. Furthermore, its comparative analysis with the state-of-the-art methods demonstrated its efficacy and reliability, which was proven by 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.
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Gupta et al. (2022) studied Heart rate estimation (n=196). U-LMA (Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm) vs. State-of-the-art rPPG methods and ground truth contact sensors was evaluated on Heart rate estimation accuracy (Pearson correlation and error metrics). The proposed U-LMA method accurately estimated heart rate from facial videos, achieving high Pearson correlation coefficients (0.92 to 0.94) with ground truth sensors across constrained, motion, and illumination variation scenarios.
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