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

Abstract

Monitoring respiration is vital for personal diagnosis of chronic diseases. However, the existing respiratory sensors have severe limitations, 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-physiological 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.