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respiratory waveforms, automated pattern classification results, and analysis of signal features [Figure 5E].
The interface offers both summary dashboards and event-level detail to support rapid decision-making by
flight personnel or ground operators. When the automatic pattern classification results in ‘dyspneic
respiration’, the system automatically triggers alerts [Supplementary Figure 17], enabling prompt
intervention in potential hypoxia or respiratory distress scenarios. An algorithmic analysis of the descending
slope also allows the system to identify inhalation and exhalation phases and distinguish between genuine
respiratory signals and artifacts caused by breath-holding, thereby providing a foundation for oxygen supply
regulation in the mask. This integration enables adaptive life support control based on classified respiratory
patterns, enhancing pilot safety during high-altitude operations where rapid physiological changes can have
serious consequences. In summary, the combined signal-processing, classification, and user-interface
components produce a closed-loop monitoring solution that is accurate, timely, and suited for critical
mission respiratory support.

CONCLUSIONS
In summary, we developed a machine learning-assisted real-time and high-sensitivity respiratory dynamics
monitoring system designed for deployment in demanding extreme scenarios to ensure continuous,
high-fidelity assessment of breathing patterns. To enhance the sensitivity of the triboelectric sensor, we apply
plasma treatment to triboelectric fibers sewn into a flexible textile and embedded in a standard oxygen mask.
This approach yields a response time of 96 ms, a sensitivity of 2.02 V·kPa , and a 420% increase in output
-1
voltage. A miniaturized acquisition module performs multimodal feature extraction and intelligent
algorithm-driven classification of six respiratory patterns with 97.2% accuracy, enabling precise
discrimination of authentic breathing signals from artifacts such as speech or coughing. This integrated
platform provides continuous, accurate tracking of respiratory parameters without compromising comfort or
mask functionality, achieving superior sensitivity and wearability compared with prior art. It therefore
supplies critical decision-support data for adaptive oxygen regulation and ground command systems in
aviation, critical care, and telemedicine applications.

DECLARATIONS
Acknowledgments
The authors thank Ms. He Dan at the National Innovation Platform for Industry-Education Integration of
Energy Storage Technology, Xi’an Jiaotong University, for her support with the XPS tests. The authors also
thank Ms. Guo Hang at the Instrument Analysis Center of Xi’an Jiaotong University for assistance with SEM
and AFM analyses.

Authors’ contributions
Conceived the research and supervised all aspects of the work: Fang, Y.; Xu, F.; Gan, J.
Provided the physiological characteristics of respiratory dynamics under extreme conditions: Wang, J.; Lin,
T.
Discussed the device structure and fabrication: Zhao, J.; Pan, X.; Gan, J.
Fabricated the textile triboelectric sensor, conducted the measurements, simulated the electric potential
distributions, and analyzed the raw data: Zhao, J.; Pan, X.; Yuan, M.; Long, Y.; Niu, Y.
Conducted the machine learning for respiratory pattern recognition: Zhao, J.; Sun, Y.
Designed the wireless pulse monitoring system and developed the ‘Real-time Respiratory Monitoring System’
APP program: Zhao, J.
Prepared the manuscript: Zhao, J.; Pan, X.
Zhao, J. and Pan, X. contributed equally to this work. All of the authors read, edited, and approved the final
version of the manuscript.

Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable
request.