{"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/remote-photonic-sensing-of-blood-oxygen","title":"Remote photonic sensing of blood oxygen saturation via tracking of anomalies in micro-saccades patterns","arxiv_id":null,"date":"2021-01-20","proceeding":null,"authors":["Zeev Kalyuzhner","Sergey Agdarov","Aviya Bennett","Yafim Beiderman","and Zeev Zalevsky"],"abstract":"Speckle pattern analysis has been found by many researchers to be applicable to remote sensing of various biomedical parameters. This paper shows how analysis of dynamic differential speckle patterns scattered from subjects’ sclera illuminated by a laser beam allows extraction of micro-saccades movement in the human eye. Analysis of micro-saccades movement using advanced machine learning techniques based on convolutional neural networks offers a novel approach for non-contact assessment of human blood oxygen saturation level (SpO2). Early stages of hypoxia can rapidly progress into pneumonia and death, and lives can be saved by advance remote detection of reduced blood oxygen saturation.","url_abs":"https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-29-3-3386&id=446758","url_pdf":"https://www.osapublishing.org/DirectPDFAccess/66D0044C-0695-AF16-39EB495CF5393BF9_446758/oe-29-3-3386.pdf?da=1&id=446758&seq=0&mobile=no","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":"remote-photonic-sensing-of-blood-oxygen","repo_url":"https://github.com/zeevikal/SpO2-speckle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}