在三维人体模型上模拟心脏信号以开发光电式血压计

Danyi Wang, J. Chahl
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摘要

简介基于图像的心率估计技术为医疗保健监测提供了一种非接触式方法,可改善数百万人的生活。为了全面测试或优化基于图像的心率提取方法,数据集应包含大量因素,如身体运动、光照条件和生理状态等。然而,收集具有完整参数的高质量数据集是一项巨大的挑战:本文介绍了一种基于人体三维(3D)表示的仿生人体模型。通过将合成心电信号和人体非自主运动整合到三维模型中,使用五种著名的传统方法和四种深度学习 iPPG(成像心动图)提取方法对渲染视频进行测试:为了与现实世界中的不同情况进行比较,在每个三维人体上创建了四种常见场景(静止、表情/说话、光源变化和身体活动)。三维人体可以有任何外观和不同的肤色。从合成人和真人视频中提取的信号具有很高的一致性--所选 iPPG 方法的性能优缺点在真人和三维人身上都是一致的:这项技术能够利用精确控制的参数和干扰,在各种场景下生成合成人。此外,它还具有相当大的潜力,可用于测试和优化基于图像的生命体征方法,以应对难以获得可靠地面实况测量结果的挑战性情况,例如无人机救援。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulating cardiac signals on 3D human models for photoplethysmography development
Introduction: 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.Methods: 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.Results: 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.Discussion: 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.
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