从使用空间时间深度学习的热视频数据估计呼吸速率
Mohsen Mozafari1, Andrew J Law1,2, Rafik A Goubran1
1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的深度学习方法,用于从热视频中准确估计呼吸速率 (RR). 这种方法实现了最先进的准确性,使得隐私保护的远程健康监控成为可能.
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:
- 远程健康监控需要保护隐私的方法.
- 呼吸速率 (RR) 的估计对于健康评估至关重要.
- 热成像为生理监测提供了一个非侵入性的数据源.
研究的目的:
- 开发一个端到端的深度学习模型,使用热视频数据进行准确的RR估计.
- 引入一种新的损失函数,解决呼吸测量中的相位变化.
- 为了评估模型在各种条件下的表现,包括面罩的使用.
主要方法:
- 使用检测变压器 (DeTr) 进行面部区域识别.
- 采用3D卷积神经网络和双向LSTM用于呼吸信号提取.
- 引入了一个新的损失函数,它结合了负最大交叉相关性和绝对频率峰值差异.
主要成果:
- 提出的方法实现了每分钟1.6次呼吸的平均误差.
- 在四个测试条件 (坐着/站着,戴/不戴口罩) 中,超过了现有的RR估计模型.
- 从热视频中证明了RR估计的最先进的准确性.
结论:
- 开发的深度学习方法提供了高度准确和保护隐私的RR估计.
- 该方法适用于远程健康监测中的实时应用.
- 热视频分析为非接触式生理监测提供了一个有希望的途径.
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