Heart Rate Monitoring From Smartphone Neck Videos Using Remote Photoplethysmography

Abstract Remote photoplethysmography (rPPG) enables contactless estimation of physiological signals from skin videos, offering a promising solution for unobtrusive cardiovascular monitoring. While most rPPG studies have focused on facial regions, privacy concerns limit their broader applicability. In this study, we explore the neck area as an alternative region of interest (ROI) for rPPG-based heart rate (HR) estimation, leveraging its proximity to major blood vessels. Video recordings of the neck of 15 adult subjects were recorded and processed using six rPPG methods: GREEN, CHROM, POS, OMIT, ICA, and LGI. HR estimates derived from each method were compared against those calculated from a gold-standard fingertip PPG using Bland–Altman and correlation analysis. Among all methods, GREEN method demonstrated the best agreement, with a bias of −0.82 bpm and limits of agreement (LoA) of −8.86–7.22 bpm, followed closely by POS (bias = −0.10 bpm; LoA = −8.50–8.30 bpm). Both methods also exhibited strong linear correlation with reference HR (r = 0.95), indicating excellent consistency. Spatial analysis of the neck region identified localized artifacts due to swallowing, light illumination, and vascular asymmetry as potential sources of signal degradation in certain cases. These findings demonstrate the viability of neck-based rPPG for HR monitoring while highlighting the importance of method selection and region-specific optimization. The study provides valuable insights for developing robust, privacy-conscious rPPG systems in clinical and remote healthcare applications.