Smart Respiratory Mask Selector: AI and Facial Scan Analytics for Personalised Mask Fitting - Bathani, Ronakkumar (International Journal of Computational and Experimental Science and Engineering, 2023)
This paper aims to assess the potential of a Smart Respiratory Mask Selector to combine artificial intelligence (AI) and facial scan analytics to provide customised masking to facilitate healthcare and industrial users. Traditional techniques of respirator fitting do not consider the craniofacial variations, causing the loss of seal integrity and decreased resistance to airborne pathogens. The study utilises a secondary approach that involves combining clinical trials on fit testing, ergonomic tests, and facial recognition standards to address the issue of the advantages of AI-based personalisation in a critical manner. Findings suggest that the system has better respiratory fit accuracy, and minimises microleakage by more than 40 percent and is still within NIOSH and EN 149 standards. Facial landmark observation was found to be very specific, and thus, it was found to be able to map mask-to-face interfaces effectively in different populations. The ergonomic results showed an increase in comfort measures, alleviation of skin irritation caused by pressure, and increased compliance with a long-term application. Notably, the algorithmic suggestions of the system guaranteed regulatory adherence and considered the difference in demographics that are usually ignored in standardised designs. Taken together, these results affirm the notion that the AI-assisted mask-fitter is effective in improving the level of both safety and convenience, and it is a scalable intervention in terms of infection control and occupational wellness. The research finds that the inclusion of biometric analytics in respiratory protection is an important innovation in both the accuracy of public health and frontline safety.