多任务深度学习方法用于非接触式同步心率和氧和估计
概括
一个新的多任务神经网络从视频中估计心率 (HR) 和外周氧和度 (SpO2). 这种基于摄像头的方法为传统脉冲氧计提供了卫生,非接触的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 脉冲氧计是监测心率 (HR) 和外周氧和 (SpO2) 的标准.
- 非接触式,基于摄像头的生命体征监测正在获得吸引力,以改善卫生和舒适.
研究的目的:
- 引入和验证一种新的多任务神经网络,用于从多光谱视频中同时,无接触地估计HR和SpO2.
- 通过使用专门的网络架构,利用人力资源和SPO2之间的相互关系.
主要方法:
- 在空间,时间和频率领域预处理多谱视频.
- 使用多任务神经网络与共享的特征提取器,任务特定的分支,道注意力和任务重量平衡.
- 使用3D多谱摄像系统对23名健康参与者进行验证.
主要成果:
- 在一个离开一个参与者的验证中,HR的平均绝对误差为2.97 bpm,SpO2的平均误差为2.34%.
- 与商业脉冲氧计相比,皮尔森相关系数为HR的0.54和SpO2的0.68.
- 证明了良好的精度和与现有的脉冲氧计方法的一致性.
结论:
- 拟议的方法显示出作为同时测量HR和SpO2的卫生和舒适替代方案的潜力.
- 这种基于摄像头的生命体征监测方法提供了一个有希望的非接触式解决方案.
- 进一步开发可以增强非接触式健康监测能力.
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