Machine learning-enabled on-mask triboelectric textile electronic system for real-time respiratory dynamics monitoring

Abstract

Real-time and accurate respiratory monitoring is crucial in extreme conditions, such as high-altitude aviation, critical care, and hazardous occupations, where subtle respiratory changes may rapidly escalate into life-threatening events. However, existing respiratory support systems are often cumbersome, insensitive to nuanced breathing patterns, or susceptible to environmental interference. Herein, we introduce a highly sensitive, plasma-modified triboelectric textile sensor integrated into an oxygen mask for real-time respiratory dynamics monitoring. By engineering nanoscale surface roughness and surface modification via plasma treatment, the sensor achieves a remarkable 420% enhancement in output voltage, yielding high sensitivity (2.02 V·kPa^-1), rapid response (96 ms), and excellent stability (over 95% signal retention after 90 days). Integrated with a machine learning-assisted classifier, the system achieves 97.2% accuracy in respiratory pattern recognition, while automatically discriminating authentic breathing signals from artifacts. With a customized electronic circuit and an application terminal, the on-mask intelligent system provides immediate feedback for adaptive oxygen regulation. This capability is of paramount importance for improving oxygen-management efficiency and safeguarding the lives of personnel operating under extreme conditions.

Keywords

Triboelectric sensors / textile electronics / plasma treatment / respiratory monitoring / machine learning

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