Physiological Modeling With Multispectral Imaging for Heart Rate Estimation

Heart rate (HR) is a key parameter in evaluating the physiological and emotional states of a person. In this paper, we propose a novel video-based heart rate (HR) estimation method based on physiological modeling with multispectral imaging. To capture blood volume pulse (BVP) associated with a person’s heartbeat, we utilize a camera that records multispectral video consisting of red, green, blue, and near-infrared information. The novelty of the proposed method is the incorporation of a physiological BVP model into a multispectral HR estimation framework. The integration of a physiological model-based BVP signal extraction scheme into an adaptive multispectral framework enables the suppression of noise derived from ambient light and the accurate extraction of the BVP signal, thereby enhancing HR estimation performance. The experiments using RGB/NIR video datasets demonstrate the effectiveness of the proposed method.