## 5 Conclusions  
In summary, this paper presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behavior. The respiratory monitoring system consists of a triboelectric respiratory sensor; circuit board hardware for real-time data acquisition, preprocessing, and wireless transmission; a machine learning algorithm to enhance the recognition accuracy of respiratory behaviors; and a mobile terminal app to display the dynamic respiratory curves and statistical data. The triboelectric respiratory sensor--fabricated by the screen-printing method--is thin, lightweight, non-invasive, and made of harmless and odorless materials. It is clearly responsive to the frequency and intensity of respiratory airflow and can easily adjust the electrical output. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. Thus, this work provides an effective and convenient means of monitoring respiratory health.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13063885/s1](https://www.mdpi.com/article/10.3390/app13063885/s1)]([https://www.mdpi.com/article/10.3390/app13063885/s1](https://www.mdpi.com/article/10.3390/app13063885/s1)), Figure S1: Flow chart of the self-powered triboelectric respiratory sensor fabricated by screen printing; Figure S2: SEM images of the screen-printed Ag layer and the nylon layer on the textile; Figure S3: Cross-sectional SEM images of the screen-printed triboelectric respiratory sensor; Figure S4: The weight of the textile printed with the triboelectric respiratory sensor and the pure textile of the same size; Figure S5: Circuit diagram of the self-built portable data-acquisition circuit board; Figure S6: The first response and recovery time of the respiratory sensor for exhalation and inhalation airflow under 0.5 Hz; Figure S7: Output current and charge curves at different operating frequencies, from 0.5 to 3 Hz; Figure S8: The effect of 20-50 \({}^{\circ}\)C temperature on the electrical output characteristics of the respiratory sensor; Figure S9: The training process of the decision tree algorithm; Figure S10: Randomly selected 1 min signal periods of five respiratory signal curves used for the recognition accuracy test of the decision tree algorithm. The test data set included 29 laughing samples, 149 normal breathing samples, 77 deep breathing samples, 64 coughing samples, and 76 sneezing samples. Table S1: Cost of each layer of 4 \(\times\) 4 cm\({}^{2}\) respiratory sensor.  
**Author Contributions:** C.Z.: Conceptualization, Investigation, Data curation, Methodology, Visualization, Writing--original draft. L.Z.: Conceptualization, Methodology, Formal analysis, Writing--review and editing. Y.T.: Software development, Visualization. B.B.: Software development. D.L.: Supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 62105238.  
**Institutional Review Board Statement:** This study did not involve humans or animals.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.