# Direct Integration of Ionic Liquid Gel Sensors onto Microfibrous Face Mask Substrates for Wearable Respiratory Health Monitoring

## TL;DR

This paper introduces a new way to integrate sensors into face masks for monitoring breathing and health at home.

## Contribution

The novel integration of ionic liquid gel sensors directly onto microfibrous face masks improves wearable respiratory health monitoring.

## Key findings

- ILG sensors show superlinear current-humidity dependence, increasing sensitivity at high humidity levels.
- ILG films remain functional under 120% mechanical strain and maintain sensing capabilities.
- AI analysis of breathing patterns detected coughs with 91% accuracy using mask-integrated sensors.

## Abstract

Mask-integrated respiratory sensors are promising for
noninvasive
monitoring of respiratory health outside of clinical settings, for
example, to support at-home patient monitoring and telemedicine. Studies
have shown the feasibility of attaching flexible and off-the-shelf
sensors to face masks; however, the potential benefits of incorporating
sensor materials directly onto the breathable microfibrous mesh of
face masks have hardly been explored. In this work, we integrate respiratory
sensors on flat polyethylene terephthalate substrates and microfibrous
polypropylene face masks using an ionic liquid gel (ILG) to detect
changes in local humidity during breathing. Under a DC voltage, the
ILG sensors exhibit a superlinear dependence of current on humidity,
resulting in enhanced sensitivity at the high humidity levels found
in exhaled breath. By varying the composition of the ILG, we find
an inverse relationship between the sensing signal strength and sensing
kinetics, offering insights into the sensing mechanisms. Freestanding
ILG films are found to be resilient under mechanical strain with humidity
sensing still possible at 120% strain. Devices that are scalably integrated
onto face masks produce sensors that are four times less sensitive
to extreme bending at an angle of 150°. Preliminary artificial
intelligence (AI) analysis of breathing patterns identifies coughs
that are interspersed with regular breathing with 91% accuracy, showing
the potential of AI-supported wearable masks for autonomous symptom
tracking.