深度学习和远程光电测量技术为非接触式生理测量技术的进步提供了动力
Wei Chen1, Zhe Yi1, Lincoln Jian Rong Lim2,3
1Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Frontiers in bioengineering and biotechnology
|August 6, 2024
概括
使用计算机视觉 (CV) 的非接触式生命体征监测提供了方便的患者观察. 本综述涵盖了人工智能在远程光电显微镜 (rPPG) 中的进步,以进行准确的健康评估.
科学领域:
- 医学技术 医学技术 医学技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 传统的生理监测限制了患者的运动.
- 无接触方法提供了方便的,持续的监控.
- 标准化的挑战阻碍了远程医疗保健应用.
研究的目的:
- 审查使用计算机视觉 (CV) 进行非接触式生理测量的人工智能 (AI) 方法.
- 概述了无接触式生命体征监测技术的最新发展.
主要方法:
- 审查传统和深度学习 (DL) 算法用于远程成像光电显微镜 (rPPG).
- 分析处理由于移动,照明和设备变化的视频信号异质性的方法.
主要成果:
- 人工智能驱动的CV技术的概述,用于非接触式生命体征监测.
- 测量皮肤输液,呼吸率,血液中氧气和度,心率,心率变化和血压方面的进展概述.
结论:
- 人工智能和CV显示出对非侵入性远程患者监测的重大前景.
- 需要进一步的标准化,以充分整合非接触式生命体征监测到远程医疗.
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