{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/distanceppg-robust-non-contact-vital-signs","title":"DistancePPG: Robust non-contact vital signs monitoring using a camera","arxiv_id":"1502.08040","date":"2015-02-27","proceeding":null,"authors":["Mayank Kumar","Ashok Veeraraghavan","Ashutosh Sabharval"],"abstract":"Vital signs such as pulse rate and breathing rate are currently measured\nusing contact probes. But, non-contact methods for measuring vital signs are\ndesirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ\nhealth tracking (e.g. on mobile phone and computers with webcams). Recently,\ncamera-based non-contact vital sign monitoring have been shown to be feasible.\nHowever, camera-based vital sign monitoring is challenging for people with\ndarker skin tone, under low lighting conditions, and/or during movement of an\nindividual in front of the camera. In this paper, we propose distancePPG, a new\ncamera-based vital sign estimation algorithm which addresses these challenges.\nDistancePPG proposes a new method of combining skin-color change signals from\ndifferent tracked regions of the face using a weighted average, where the\nweights depend on the blood perfusion and incident light intensity in the\nregion, to improve the signal-to-noise ratio (SNR) of camera-based estimate.\nOne of our key contributions is a new automatic method for determining the\nweights based only on the video recording of the subject. The gains in SNR of\ncamera-based PPG estimated using distancePPG translate into reduction of the\nerror in vital sign estimation, and thus expand the scope of camera-based vital\nsign monitoring to potentially challenging scenarios. Further, a dataset will\nbe released, comprising of synchronized video recordings of face and pulse\noximeter based ground truth recordings from the earlobe for people with\ndifferent skin tones, under different lighting conditions and for various\nmotion scenarios.","url_abs":"http://arxiv.org/abs/1502.08040v2","url_pdf":"http://arxiv.org/pdf/1502.08040v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"distanceppg-robust-non-contact-vital-signs","repo_url":"https://github.com/arviram/pulse_from_video","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1502.08040","atlas_url":"https://app.syntology.ai/?focus=1502.08040","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}