Poster: Noninvasive Respirator Fit Factor Inference by Semi-Supervised Learning
Poster: Noninvasive Respirator Fit Factor Inference by Semi-Supervised Learning
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DOI：
10.1145/3580252.3589420
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Jinmiao Chen;Zhaohe (John) Zhang;Shangqing Zhao;Song Fang;T. Peters;Evan L. Floyd;Changjie Cai
中科院分区：
文献类型：
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作者：
Jinmiao Chen;Zhaohe (John) Zhang;Shangqing Zhao;Song Fang;T. Peters;Evan L. Floyd;Changjie Cai
流行病强调了对个人防护设备的需要，如消毒器，因为它们提供了对传染病的保护。只有当呼吸器正确安装并正确佩戴时，才能提供充分的保护。因此，密切监测和确保呼吸器适合性尤其重要，特别是在大流行期间。为了确保适当的配合和连续监测，我们提出了一种新的非侵入性的方法，使用语音信号来测量呼吸机引起的声音衰减。该方法提供了呼吸器适配系数（FF，环境空气中物质浓度与呼吸器内物质浓度的比率）的定量测量。这种方法也具有成本效益，易于实施。通过收集有限的标记和未标记语音数据、增强标记数据、提取时域和频域特征，我们使用半监督学习模型实现了高达86.24%的呼吸器适合度检测准确率。
The need for personal protective equipment, such as respirators, has been emphasized by pandemics as they provide protection against infectious diseases. Adequate protection is only possible when respirators fit properly and are worn correctly. Therefore, it is especially critical to closely monitor and ensure respirator fit, particularly during a pandemic. To ensure proper fit and continuous monitoring, we propose a new noninvasive method that uses speech signals to measure the attenuation of sound caused by the respirator. This method provides a quantitative measure of respirator Fit Factor (FF, the ratio of the concentration of a substance in ambient air to its concentration inside the respirator). This method is also cost-effective and easy to implement. By collecting limited labeled and unlabeled speech data, augmenting labeled data, extracting time and frequency domain features, we achieved up to 86.24% accuracy in respirator fit detection using semi-supervised learning model.