Integrated wearable smart sensor system for real-time multi-parameter respiration health monitoring

Monitoring respiration is vital for personal diagnosis of chronic diseases. However, the existing respiratory sensors have severe lim-itations, such as single function, finite detection parameters, and lack of smart signal analysis. Here, we present an integrated wearable and low-cost smart respiratory monitoring sensor (RMS) system with artificial intelligence (AI)-assisted diagnosis of respiratory abnormality by detecting multi-parameters of human respiration. Coupling with intelligent analysis and data mining algorithms embedded in a phone app, the lighter system of 7.3 g can acquire real-time self-calibrated parameters, including breathing frequency, apnea hypopnea index (AHI), vital capacity (VC), peak expiratory flow (PEF), and other respiratory indexes with an accuracy >95.21%. The data can be wirelessly transferred to the user's data cloud terminal. The RMS system enables comprehensive multi -phys-iological parameters analysis for auxiliary diagnosing and classifying diseases, including sleep apnea, rhinitis, and chronic lung diseases, as well as rehabilitation of COVID-19, and exhibits advantages of portable healthcare.