Remote Photoplethysmography
Also known as rPPG
Overview
Remote photoplethysmography studies how small changes in skin color and motion in camera footage can reveal pulse signals, heart rate, and sometimes respiration or oxygen saturation. Methods range from color-based signal processing and region-of-interest selection to deep temporal networks, self-supervised learning, and fusion of video with radar, near-infrared, or event-camera data. Researchers evaluate these approaches on video benchmarks and in settings such as driving, clinical monitoring, and egocentric sensing. A central challenge is reliable performance across motion, lighting, skin tones, compression, and new datasets, while imperfect sensor synchronization and limits on which waveform details video can recover complicate training and evaluation.
Latest papers
46 papers1 paper in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.