Intelligent anti-epidemic mask based on KF and ECF fusion algorithm

Area of Science:

• Engineering
• Environmental Science
• Public Health

Background:

• Rising concerns over environmental pollution and the COVID-19 pandemic necessitate advanced personal protective equipment.
• Existing mask technologies often lack integrated environmental monitoring and active protection mechanisms.

Purpose of the Study:

• To design and validate a novel smart mask with an intelligent control system for enhanced health protection.
• To develop and implement an improved filtering fusion algorithm for accurate behavior characteristic value calculation.
• To optimize the mask's structural design using fluid mechanics simulation.

Main Methods:

• Utilized Kalman filter (KF) for raw sensor data processing, followed by complementary filtering and data fusion to determine body attitude angles.
• Integrated attitude angle, acceleration, and blood oxygen concentration data to derive behavior characteristic values.
• Implemented a double closed-loop control system linking behavior characteristics to an oxygen supply fan, optimized via fluid mechanics simulation.

Main Results:

• The filtering fusion algorithm effectively processed sensor data to determine body attitude and behavior characteristics.
• The closed-loop control system dynamically adjusted oxygen supply based on user behavior and physiological data.
• Fluid mechanics simulations informed structural optimization, enhancing the mask's protective capabilities.

Conclusions:

• The developed smart mask, featuring a replaceable filter, intelligent control, and UV disinfection, offers effective protection against environmental pollutants and airborne pathogens.
• The proposed filtering fusion algorithm and control strategy represent a significant advancement in smart mask technology.
• Experimental validation confirms the system's efficacy in safeguarding user health.