{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:08Z","timestamp":1753893068415,"version":"3.41.2"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p><jats:bold>Introduction:<\/jats:bold> Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> To compare with different situations in the real world, four common scenarios (stillness, expression\/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans.<\/jats:p><jats:p><jats:bold>Discussion:<\/jats:bold> This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.<\/jats:p>","DOI":"10.3389\/frobt.2023.1266535","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T04:39:00Z","timestamp":1704861540000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Simulating cardiac signals on 3D human models for photoplethysmography development"],"prefix":"10.3389","volume":"10","author":[{"given":"Danyi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Javaan","family":"Chahl","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"R1","DOI":"10.1088\/0967-3334\/28\/3\/r01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"Allen","year":"2007","journal-title":"Physiol. 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