Conference proceeding
Poster: Noninvasive Respirator Fit Factor Inference by Semi-Supervised Learning
2023 IEEE/ACM CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES, CHASE, pp.200-202
IEEE International Conference on Connected Health-Applications, Systems and Engineering Technologies
01/01/2023
DOI: 10.1145/3580252.3589420
Abstract
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 newnoninvasive 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.
Details
- Title: Subtitle
- Poster: Noninvasive Respirator Fit Factor Inference by Semi-Supervised Learning
- Creators
- Jinmiao Chen - University of OklahomaZhaohe (John) Zhang - University of OklahomaShangqing Zhao - University of OklahomaSong Fang - University of OklahomaThomas M. Peters - Univ Iowa, Iowa City, IA USAEvan L. Floyd - OU Hlth Sci Ctr, Oklahoma City, OK USAChangjie Cai - OU Hlth Sci Ctr, Oklahoma City, OK USA
- Resource Type
- Conference proceeding
- Publication Details
- 2023 IEEE/ACM CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES, CHASE, pp.200-202
- Series
- IEEE International Conference on Connected Health-Applications, Systems and Engineering Technologies
- DOI
- 10.1145/3580252.3589420
- ISSN
- 2832-2967
- Publisher
- Assoc Computing Machinery
- Number of pages
- 3
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Occupational and Environmental Health
- Record Identifier
- 9984455947302771
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