An intelligent gateway-based cardiopulmonary health monitoring system using a CNN-LSTM model achieved 99.49% accuracy for real-time ECG signal identification at the edge.
A novel intelligent gateway-based cardiopulmonary health monitoring system using an edge-computing CNN-LSTM model achieved 99.49% accuracy for real-time ECG signal identification.
Cardiopulmonary diseases, including cardiovascular disease (CVD) and chronic obstructive pulmonary disorder (COPD), are prevalent in the elderly population. Early identification, long‐term health monitoring, and health management of cardiopulmonary disease are crucial for reversing organ damage and preventing further injury. However, most home health monitoring (HHM) devices need to be paired with a specific smartphone application, which leads to the complexity of monitoring multiple health indicators and hinders the feasibility of comprehensive multi‐indicator analysis. Therefore, this paper designed a human cardiopulmonary health monitoring system based on an intelligent gateway to reduce the dependence of HHM on smartphones, achieve synchronous monitoring of multiple health indicators, and improve usability to better serve the elderly population. The proposed system can simultaneously monitor electrocardiogram (ECG), pulmonary function, blood pressure, and blood oxygen level (SpO2); process and analyze the data in real‐time through the intelligent gateway’s edge computing power; and display the cardiopulmonary health status in real‐time. The intelligent gateway embedded a specially designed CNN‐LSTM artificial intelligence model on the STM32F429 microcontroller to realize real‐time identification of ECG signals at the edge. The accuracy of the pretrained CNN‐LSTM model for ECG signal identification is 99.49%, and the model has good performance in terms of complexity and RAM space occupied. According to the evaluation test, the system can achieve the purpose of monitoring human cardiopulmonary health, has a wide range of application scenarios, and has great value in promotion and application.
Zhao et al. (Sun,) conducted a other in Cardiopulmonary diseases (CVD and COPD). Intelligent gateway based human cardiopulmonary health monitoring system was evaluated on Accuracy of the pretrained CNN-LSTM model for ECG signal identification. An intelligent gateway-based cardiopulmonary health monitoring system using a CNN-LSTM model achieved 99.49% accuracy for real-time ECG signal identification at the edge.