A novel face-based non-contact technique using multiple linear regression and Hilbert-Huang transform outperformed the current state-of-the-art method for heart rate evaluation with higher accuracy.
Does a face-based heart rate signal decomposition using multiple linear regression improve the accuracy of heart rate evaluation compared to current state-of-the-art methods?
A novel non-contact, face-based heart rate measurement technique using multiple linear regression and HHT demonstrates improved accuracy and reduced variance compared to existing methods.
Contact measurements of the cardiac pulse using the conventional electrocardiogram equipment requires patients to wear adhesive gel patches or chest straps that can cause skin irritation and discomfort. Commercially available pulse oximetry sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can be painful to use. Therefore, a novel robust non-contact technique is developed for the evaluation of heart rate variation. According to the periodic variation of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as heartbeats cause changes to blood volume in the blood vessels in the face, a reflectance signal is decomposed from consecutive frames of the green channel of the facial region. Furthermore, ensemble empirical mode decomposition of the Hilbert-Huang transform (HHT) is used to acquire the primary heart rate signal while reducing the effect of ambient light changes. The effective instantaneous frequencies from intrinsic mode functions decomposed by HHT are implemented by the multiple-linear regression model to evaluate heart rates before the frequencies were rectified by maximum likelihood method assuming Poisson distribution, and the minimum elapse time for heart rate evaluation is also evaluated in the estimate process. Experimental results show that our proposed approach provides a convenient non-contact method to evaluate heart rate and outperforms the current state-of-the-art method with higher accuracy and smaller variance.
Lin et al. (Wed,) conducted a other in Heart rate evaluation. Face-based heart rate signal decomposition using multiple linear regression and HHT vs. Current state-of-the-art method was evaluated on Heart rate evaluation accuracy and variance. A novel face-based non-contact technique using multiple linear regression and Hilbert-Huang transform outperformed the current state-of-the-art method for heart rate evaluation with higher accuracy.