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Page 4 of 15 Zhao et al. Soft Sci. 2026, 6, 4

Simulation of surface potential distribution
Finite element simulations were conducted using COMSOL Multiphysics (v. [6.3], COMSOL Inc.,
Stockholm, Sweden) to investigate the influence of surface roughness on triboelectric performance.
Two-dimensional axisymmetric models were developed to represent smooth and rough electrode surfaces.
The latter was characterized by a periodic array of isosceles triangles - with a base width of 0.2 μm and a
height of 0.1 μm - consistent with the surface topography observed via atomic force microscopy (AFM) of
plasma-treated fibers. Material properties for the epoxy resin and PVDF were sourced from the COMSOL
built-in material library. Surface charge densities of ± 3 μC·m were applied to represent triboelectric
-2
charging, with opposite polarities assigned to the epoxy and PVDF surfaces. The contact-separation process
was simulated by varying the electrode separation distance from 0.1 to 5 μm in 0.1 μm increments. The
surface potential distribution was then derived from a steady-state calculation.

Collection of respiratory data
Volunteers were fitted with pilot oxygen masks incorporating triboelectric sensors positioned above the
mouth-nose airflow channel. Data acquisition was performed using an STM32 microcontroller
(STMicroelectronics, Geneva, Switzerland) with a sampling rate of 100 Hz. Signal conditioning included
amplification using an operational amplifier (OP07CP, Texas Instruments, Dallas, TX, USA) and low-pass
filtering with a cutoff frequency of approximately 15.9 Hz to remove noise. The processed signals were then
transmitted to the host computer via serial communication for further analysis.

Feature extraction and classification of respiratory signals
Machine learning-based classification of respiratory patterns was implemented using a backpropagation
neural network architecture based on features extracted from the respiratory signals. Feature extraction was
performed in three domains to capture comprehensive characteristics of the respiratory signals.
Time-domain features included breathing frequency, signal amplitude (representing breathing depth),
inhalation time, exhalation time, and discrete signal energy calculated as the sum of squared voltage samples.
Frequency-domain analysis employed the Fast Fourier Transform to determine the dominant frequency and
its ratio to the total spectral power. Time-frequency domain features were extracted using the Short-Time
Fourier Transform with a 512-sample window. The neural network consisted of an input layer with six
neurons corresponding to the extracted features, two hidden layers with ten neurons each, and an output
layer with six neurons representing the respiratory pattern classes. For network training, the Adam optimizer
was adopted to optimize model parameters, and the cross-entropy loss function was used to quantify the
discrepancy between predicted and true respiratory pattern labels. To enhance the model’s generalization
across subjects and its reliability for practical applications, a dataset of 5,500 labeled samples was compiled,
collected from five distinct subjects and covering six target respiratory patterns. A portion of these samples
was acquired under high electromagnetic field conditions and simulated flight-related stress, such as motion
and vibration, to emulate interference in real flight environments. The dataset was partitioned to ensure
unbiased performance evaluation: 5,000 samples were allocated to the training set, and the remaining 500
samples served as an independent held-out test set. The training set was further processed using five-fold
stratified cross-validation to ensure robust model selection and hyperparameter tuning. The held-out test set
was used exclusively for final performance evaluation to reflect the model’s real-world predictive capability.

Customized pilot respiratory monitoring user interface
A real-time monitoring interface was developed using the PyQt6 framework (Riverbank Computing,
Dorchester, UK) to provide comprehensive visualization and analysis capabilities for pilot respiratory
assessment. The interface integrated multiple display modules, including real-time waveform visualization,
automated pattern classification results updated at the completion of each respiratory cycle, feature
parameter tracking, and an alert management system for abnormal respiratory patterns.