使用便携式设备上的RGB红外传感器检测呼吸道感染
Zheng Jiang1,2, Menghan Hu3,2, Zhongpai Gao1,2
1Institute of Image Communication and Information Processing, Shanghai Jiao Tong University Shanghai 200240 China.
概括
这项研究引入了一种使用RGB红外传感器的非接触方法,用于选口罩个体的呼吸系统健康状况. 该系统达到83.69%的准确性,有助于早期检测潜在的COVID-19携带者.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 由于COVID-19的流行,需要快速检测呼吸道感染.
- 戴口罩是常见的,使传统的健康查方法复杂化.
- 异常的呼吸状态是COVID-19等病毒感染的关键指标.
研究的目的:
- 开发一种便携式,非接触式的方法,用于对戴口罩的人进行呼吸系统健康查.
- 使用RGB红外传感器和机器学习分析呼吸系统特征.
- 为早期检测和隔离潜在病毒载体提供一种工具.
主要方法:
- 利用面部识别来捕获面具个体的呼吸数据.
- 采用双向GRU神经网络,具有用于健康状况分析的注意力机制.
- 使用真实世界的数据集验证了系统.
主要成果:
- 该模型在识别呼吸系统健康状况时实现了83.69%的准确性.
- 灵敏度达到了90.23%,特异性为76.31%.
- 证明了使用便携式RGB红外传感器进行预先选的可行性.
结论:
- 拟议的系统提供了一种可行的非接触方法,用于在口罩人口中预先查呼吸道感染.
- 这项技术为临床试验提供了基础,以对抗COVID-19大流行.
- 强调传感器技术和人工智能在公共卫生监测中的潜力.
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