Contactless heart rate and heart rate variability estimation from neck videos

Video-based pulse extraction is a non-contact technique for estimating physiological signals from video recordings. While traditional approaches focus on facial regions due to their high vascularity and accessibility, this study explored the feasibility of extracting pulse signals from neck region, an area less studied in the literature. Nech and chest video data were collected from 14 healthy subjects during breath-hold. A region of interest on the neck was tracked using a template-based algorithm, and pixel intensity variations within this region were processed using six established video-based pulse extraction methods: GREEN, the chrominance-based method, plant-orthogonal-to-skin method, orthogonal matrix image transformation method, independent component analysis method, and local group invariance method. The extracted pulse signals were validated against synchronized electrocardiogram (ECG) recordings. Among the methods, GREEN exhibited the highest agreement with ECG-derived heart rate (HR), with a bias of -4.89 bpm and limits of agreement (LoA) ranging from 29.97 to 20.17 bpm. After excluding two subjects with significant noise interference, the agreement improved to a bias of -1.42 bpm and LoA of -4.95 to 2.09 bpm. HR variability (HRV) was assessed using SDNN. SDNN values from GREEN method were generally higher than those from ECG, likely due to signal differences, motion artifacts, and the short duration (1015 seconds) of the recordings, which may have inflated variability estimates. Findings suggested that the neck region, particularly when using the GREEN method, is a viable site for video-based pulse signal extraction and HR estimation. However, variations in SDNN underscore the need for further refinement in video-based pulse extraction techniques. Expanding this technology to the neck region offers promising applications in remote health monitoring and physiological assessment, particularly to mitigate the privacy concerns of facial recordings.