Smart Face Mask Based on Nanoparticle Humidity Sensor for Breath Monitoring

Respiratory monitoring has important applications in healthcare by enabling early disease detection. However, designing humidity sensors for integration into smart masks poses unique challenges. The sensor must have fast response and recovery times faster than normal breathing rates. It must also maintain stability when exposed to atmospheric conditions. In this study, we developed gold nanoparticle-based humidity sensors for smart masks. We tested the sensors' I–V curves under varying humidity levels. Remarkably, the sensors achieved the fastest response time of 0.2 seconds and recovery unaffected by ambient airflow. When integrated into smart masks, the sensors successfully discriminated and recorded different breathing patterns, including oral, nasal, deep, fast, breath-hold, cough, and restorative signals. Analysis of the normalized amplitudes and frequencies of patterns using continuous wavelet transform revealed comprehensive respiratory monitoring capabilities. Overall, this research provides a stable, low-cost smart mask platform for respiratory monitoring. Moving forward, such sensors show promise for non-invasive disease screening and personalized respiratory care.