Respiratory protection is mandatory if you work in a high-risk field. Healthcare, construction, manufacturing, and military personnel are all at risk of respiratory damage without a tight-fitting respirator.

New innovations for AI in respiratory care and PPE fit tests are bringing better precision, a safer seal, data security, and enhanced comfort to OSHA-accepted fit tests. Learn about Smart Fit AI now. 

Source: MIRA Safety

A high-quality respirator must fit correctly to filter out hazardous particulates. Improper fit can allow contaminants to leak in around the edges of the respirator. This is why respirator fit testing is closely regulated by several agencies, including:

• The Occupational Safety and Health Administration (OSHA)
• National Institute for Occupational Safety and Health (NIOSH) 
• American Society for Testing and Materials (ASTM) 

It's also why using a full facepiece respirator instead of a half-mask is generally recommended when performing tasks in a hazardous environment. These agencies mandate several standards for respirator use, including a fit test for each user that includes:

• Qualitative testing - checking to see if the wearer can smell a strong substance through the filtering facepiece 
• Quantitative testing - Using specialized equipment to track the amount of air leakage into the full facepiece
• Wearer training - each user must be trained to properly use the equipment
• Medical evaluation  - the person wearing the respirator must pass an evaluation saying that they are safe to wear a respirator 
• Maintenance - Regulations mandate that personal protective equipment be regularly inspected to determine proper working condition

Of course, with different face shapes and sizes, finding the right respirator is often a bit difficult. It's crucial that the mask establish a complete face seal for each individual user to maintain safety and meet regulatory compliance.

Quality respirators are adjustable at several points and must be tested to ensure a complete air seal. This is often difficult for the end-user to do alone and can lead to a poor fit, which poses severe health and safety hazards.

Recent updates in fit testing technology have made it easier for onsite managers and end-users to create a custom fit with accuracy. Using real-time guidance from a paired app, users can make small adjustments with instant feedback. This ensures confidence in the proper working order and fit of the mask. It also ensures compliance with proper test protocol. 

Developing this technology is crucial to ensure that smaller organizations, local law enforcement, and others have access to the fit test protocols they need to stay safe. 

Source: MIRA Safety

History of Respirator Fit Testing 

Respirator fit testing evolved significantly over the years. Qualitative testing has been considered the "gold standard" in testing. Newer methods involved using an aerosol compound to detect what could be leaking through filtering facepiece respirators and ensure a tight seal.

Positive and negative pressure tests are also common. When covering the exhalation vent, the wearer inhales deeply, testing to see if the facepiece caves slightly inward. Similarly, when breathing out, the respirator should bulge outward slightly.

During the Covid-19 pandemic, remote methods of determining facepiece fit became a necessity. However, transmitting data via traditional video calls presents a data security concern. 

AI-powered solutions surpass traditional fit methods because they don't require a fit specialized to come on-site with the wearer. Nor is information about the wearer transferred over the internet. Rather, personalized fit solutions are local to the user's device and remain secure. 

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Similarly, AI-driven fit programs like Smart Fit AI provide more precision than is typically possible with either qualitative or quantitative methods.

Qualitative Fit Testing Procedure

Qualitative fit tests (QLFT) are designed to ensure that the individual wearer is wearing the respirator correctly. 

• The person administering the test must be qualified to perform the test and document the results
• A strong-smelling substance is released into the room
• The wearer performs normal activities for about 15 minutes
• If they can detect the smell, then there is a problem with the fit
• The fit-test must adhere to the ANSI/AIHA/ASSE Z88.10-2010 Respirator Fit Testing Methods guidance published by OSHA 
• The fit must meet or exceed ASTM F3407 Standard for Respirators 

While mobile fit test programs have made some headway in expanding access, it is often difficult to meet the guidelines for annual fit checks for each wearer. 

Furthermore, the experience is subjective. It can be prone to user error. 

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Quantitative Fit Testing Procedure 

Quantitative test procedures (QNFT) use specialized equipment to precisely track any leaks in air-purifying respirators.

These tests usually use a device called a Condensation Nuclei Counter (CNC). This device measures the concentration of particles inside the respirator to make sure that they are not rising to match the particles outside the mask. 

The wearer does not provide feedback with this method, but they do perform basic motions like sitting, standing, bending, and walking. This ensures that the respirator fits properly during normal activity. 

The resulting number is converted into a measure called "fit factor." Higher scores are better, with a fit factor of 100 considered sufficient for most uses.

Source: MIRA Safety

QNFT are preferred for their accuracy, but they tend to be more expensive to administer and harder to access because of the specialized equipment and training involved. 

Technological Improvements in Respirator Fit Testing 

Technology has rapidly improved over the past few years. Much of this has been driven by the new ASTM F3407 Standard, which emphasizes a higher fit factor and increased comfort for the wearer. 

New tools to achieve this include: 

• Digital fit test systems - these make it easier for fit testers to record results and communicate recommendations to wearers
• AI and machine learning - this emerging technology uses large amounts of data (not personally identifiable) to predict issues with fit and target specific areas of improvement
• CNCs - these measure the particulates inside the respirator and compares it to the air outside, providing hard data 
• Mobile fit labs - these make fit testing more accessible to remote locations 

Since fit testing is both highly individualized and tightly regulated, technology can help bridge the gap between compliance and personalized experience. 

For example, to ensure a proper fit, the respirator needs to fit tightly across the wearer's face. Face shape can change over time, when one grows or shaves a beard, has dental surgery, or loses or gains weight. This means that testing needs to be performed annually and can't rely on old measurements. 

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Emerging technologies in fit testing have the dual benefit of increasing accuracy and speed in the process, ensuring effective respiratory protection for each individual wearer. 

Introduction to AI in Respiratory Care 

AI offers promising solutions to some of the major problems in respiratory care. Large language models can quickly analyze large quantities of data to find patterns. Applied in a healthcare setting, that means that AI can quickly deliver recommendations and adjust to new input.

 For respiratory care, AI provides almost instant feedback on user experience, like if a patient is using an inhaler correctly or if a respirator-wearer is adjusting their respirator properly. Users can ask questions or provide their feedback on comfort level to receive instant feedback, without the need of an in-person fit adjuster present. 

 For privacy-sensitive programs like Real Fit AI, this real-time feedback is located entirely on the user's personal device, meaning that data remains secure and is not personally identifiable when used with large language models.

AI-Based Respirator Fit Testing Solutions 

AI fit test uses are still being explored - and initial results are promising. AI applications use large amounts of data to give general recommendations and then blend that with real time feedback for personalized fit guidance. 

We'll explore some ways that AI solutions can make fit testing for employees and law enforcement easier, more accurate, and more accessible. 

Smart AI Real-Time Fit 

Machine learning algorithms can use infrared to detect face shape and leaks to the respirator. This allows the application to instantly offer recommendations to users in real time, so they can make small adjustments until they receive a "green light" from the app. 

This instant feedback removes some of the guesswork from qualitative methods while still allowing wearers to prioritize comfort. 

AI-Assisted Apps for Home Testing 

Under traditional qualitative and quantitative measures, individual users need to wait for a trained fit specialist to provide training and perform seal checks. Most employers only provide this service once a year, leaving troubling gaps in the ability to detect improper fit and degraded equipment

Real-Time Data Collection and Feedback 

Smart Fit AI employs instant feedback combined with large data models to provide customized recommendations on fit. The technology is now so precise that it can measure key fit points down to 0.1mm accuracy. This allows the app to recommend the best-fitting mask as well as specific fit adjustments.

Moreover, the data hosted by the app provides a reference range, giving the application a jumping-off point for recommending a fit with an acceptable level of protection. The measurements taken are not linked to personally identifiable information, making it accessible for those who don't care to share sensitive information 

Source: MIRA Safety

AI's Role in Real-Time Monitoring and Adjustments 

Finding the right size respirator and AI sizing it appropriately is only the first step. It is crucial that the mask continues to fit perfectly with continued wear. 

As the mask is worn, slight pressure builds over time and can affect seal integrity. With heavy use, straps can loosen, affecting fit. 

Additionally, human faces are constantly changing in small ways that are often unseen by the human eye. Growing or shaving facial hair or slight changes in weight can affect respirator fit. 

AI has the potential to respond in real time to changes in the respirator seal and straps and in the wearer's face shape. Thanks to real-time feedback, AI could respond to changes in seal function that are too small for a human to detect. This allows for automated fit adjustment recommendations continuously - not just at an annual fit check. 

Moreover, using large data models, AI can predict potential fit issues before they even happen. For example, employees with smaller-than-average faces could be automatically recommended to a range of respirators that are more likely to fit their face shape. 

Challenges and Ethical Considerations 

The first concern you may have with AI-augmented fit checks and adjustments is privacy. With large amounts of our data openly traded by marketing companies, most people are somewhat hesitant about health-related data being gathered and stored. 

Smart Fit AI is local to the user's device. This protects users' data and makes the measurement and fit check process completely secure. Large learning models don't affiliate their data with the individual, so the statistical knowledge used to make recommendations can't be linked back to a specific person.

While this technology does increase access for remote workplaces, WiFi access is still an issue for some. Many rural areas still do not have reliable high speed internet access, which can present a challenge to using new technologies like AI. 

Source: MIRA Safety

Future Trends in Respirator Fit Testing 

The machine learning field has grown by leaps and bounds in the last few years alone. As new technology develops and is integrated into our daily lives, there are increased possibilities for health and safety applications.

Advancements in Sensor Technology 

Health-related wearables are becoming increasingly possible, with some models even able to monitor heart patients and diabetics for life-threatening incidents. With larger data sets available each year, AI will be better able to quickly detect patterns and make recommendations based on a large population. 

Additional applications for AI in a hazardous workplace setting are being explored. Some researchers suggest that AI can be integrated with current CCTV security systems.

This would allow it to recognize patterns and predict hazards before workplace accidents occur. This could augment human managers, who are often busy managing multiple tasks and don't always notice potential hazards in time to intervene. 

Potential for Broader AI Integration 

Public health officials predict AI applications for clinicians and patients. Chatbots are already a familiar feature of public health interactions, helping to direct patients to professionals who are the best fit for their needs.

Large data sets and responsive feedback could be helpful for emergency situations as well. AI can sort through a large library of images and definitions to help quickly identify life-threatening conditions for busy clinicians who need help to triage patients. 

Conclusion: The Future of AI for Preventing Respiratory Hazards 

Using a respirator is the only way to ensure maximum protection when working in hazardous environments, including public health, manufacturing, or law enforcement. 

Recent advancement in AI technology increases the expected protection provided by a tight-fitting respirator and makes proper measurement available to more users. By using real-time feedback, large language models, instant measurement technologies, and data-secure best practice, AI has the potential to enhance the safety of professionals who require respiratory protection. 

Sources:

"Overview of the ASTM F3407 Standard Test Method for Respirator Fit Capability" CDC

“The Respiratory Protection Information Trusted Source,” The National Personal Protective Technology Laboratory (NPPTL), CDC

"Key Applications, Challenges of Artificial Intelligence in Respiratory Care," American Journal of Medicine 

"The Role of Artificial Intelligence in Occupational Safety and Health Practices (OSH)" University of South Florida