Poster: Noninvasive Respirator Fit Factor Inference by Semi-Supervised Learning

• Published in: 2023 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) pp. 200 - 202
• Main Authors: Chen, Jinmiao, Zhang, Zhaohe John, Zhao, Shangqing, Fang, Song, Peters, Thomas M., Floyd, Evan L., Cai, Changjie
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.06.2023
• Subjects: COVID-19; Feature extraction; Frequency-domain analysis; Infectious diseases; MFCC; Pandemics; Personal protective equipment; Respirator fit test; semi-supervised learning; Semisupervised learning; speech signal
• ISSN: 2832-2975
• DOI: 10.1145/3580252.3589420

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.