## 3 Results
### Data Analysis and Feature-Extraction Approaches Respecting Respiratory Signals  
Figure 3a-e show the output signal curves of five typical respiratory behaviors recorded by the sensor, including normal breathing, deep breathing, coughing, sneezing, and laughing. These respiratory statuses represent the most common natural physiological behaviors. It can be seen that the electrical signal curves exhibit significantly different waveforms and intensities and provide characteristic information, including but not limited to frequency, intensity, duration, and peak value. Wavelet transform (Figure 3f-j) and Fourier transform (Figure 3k-o) are used to analyze the electrical signals [26], showing the corresponding time-frequency spectra and frequency-energy distributions, respectively. The wavelet transform and Fourier transform are both powerful tools for analyzing signals. The wavelet transform is useful for analyzing transient non-stationary signals, such as respiratory signals, and can provide a time-frequency map that directly reflects the joint time-frequency characteristics of a signal. The Fourier transform is useful for converting a signal from the time domain to the frequency domain, allowing for the identification of intense frequency bands in the signal. The Fourier transform can also be used to identify the peak energy concentration of a signal, while the wavelet transform can be used to visualize the frequency change band. Figure 2: The working mechanism and typical output performances of the triboelectric respiratory sensor. **(a,b)** Schematic illustration of the working mechanism and COMSOL finite-element simulation results for the triboelectric respiratory sensor. **(c)** Typical output voltage, current, and charge curves for the triboelectric respiratory sensor. **(d)** Output voltage results for different pressures, from 60 Pa to 3000 Pa. **(e)** Output voltage curves for different operating frequencies, from 0.5 to 3 Hz. **(f)** Output voltage curves for different sensing areas of 2 \(\times\) 2 to 6 \(\times\) 6 cm\({}^{2}\). **(g)** Cycling stability performance of the triboelectric respiratory sensor for 10,000 cycles. Both transforms have their own advantages and can be used to analyze different types of respiratory signals and extract signal features. As shown in Figure 3a,f,k, a peak signal with a voltage of 27.7 mV and a duration of 3.7 s was obtained for the normal breathing process with a 0.24 Hz energy concentration frequency. Deep breathing, which is stronger and longer than normal breathing, meanwhile, presented an output voltage of 63.8 mV, a duration of 14.3 s, and an energy concentration frequency of 0.07 Hz (Figure 3b,g,l). The original curves for coughing and sneezing were similar, but the impact force of the transient airflow generated by sneezing is significantly stronger than that of coughing, which can be verified by the peak distributions of the output voltage and energy concentration curves (Figures 3c,h,m and 3d,i,n). Due to multiple and instantaneous gas-exchange processes, laughing signals present multiple energy concentration peaks mainly concentrated in the range of 0-1 Hz (Figure 3e,j,o). Finally, a total of 12 typical feature values were extracted from the above time-domain and frequency-domain spectra. From the original time-domain spectra, the mean values, variances, standard deviations, and root mean squares and kurtosis can be extracted. From the frequency-domain spectra, based on the Fourier transform, the features of unbiased estimations, coefficients of variation, and edge factors can be extracted. From the time-domain spectra, based on the wavelet transform, the features of mean values, variances, standard deviations, and root mean squares and kurtosis can be extracted. All these features can be used to analyze the characteristics of respiratory signals and can provide valuable insights into the complex respiratory processes.