###### Abstract  
Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor--fabricated by the screen-printing method--is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the 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. This work promotes the development of wearable health monitoring systems.  
Keywords:respiratory monitoring; triboelectric sensor; decision tree model; machine learning algorithm; intelligent mask +
Footnote †: journal:  
developed a biodegradable respiratory sensor to distinguish between healthy and asthmatic, bronchitis, and chronic obstructive pulmonary disease states. [PERSON] et al. [12] reported a respiratory monitoring system that can simultaneously sample respiratory and temperature signals using both capacitive and resistive sensors. [PERSON] et al. [13] integrated a five-channel triboelectric sensing network on a mask, which can overcome environmental interference due to different facial contours and achieve a recognition accuracy of up to 100%. Among these sensors, the triboelectric sensor, based on the coupling effect of triboelectric and electrostatic induction, has great potential among wearable electronics due to its simple structure, light weight, and self-powered characteristics [14; 15; 16; 17; 18; 19]. However, there are still some challenges to overcome in order to gain a portable and sufficiently smart respiratory monitoring system. These challenges include making respiration sensors highly portable and comfortable for the human face, achieving higher accuracy to recognize complex respiratory behaviors, and improving system-level and multi-terminal respiratory monitoring applications.  
In this study, we report a portable, algorithm-assisted, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The triboelectric respiratory sensor was directly screen-printed onto a commercial mask with a thickness of 50 \(\upmu\)m and a weight of 0.05 g to capture respiratory signals (respiratory intensity and respiratory frequency) and showed high wearability and compatibility in daily use. A portable integrated circuit board was fabricated for signal acquisition, preprocessing, and wireless transmission, which can be reused, and only the low-cost respiratory sensing mask would need to be replaced in long-term use, significantly reducing the cost of the whole system. A decision tree model algorithm is used to categorize and quantify respiratory signals. A total of 12 features are extracted via a trainable classifier, and an average accuracy of 97.2% has been validated for five types of respiratory status. The real-time signals and statistical results of the respiratory status of the user can be uploaded to a server network and displayed in various mobile terminals for future body health warnings and advice.