# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 3 ECG Diagnosis Model Based on Edge Cloud Intelligent Medical Architecture  
### The Edge Cloud Intelligent Medical Architecture  
The intelligent medical system based on cloud cannot meet the urgent needs of people for daily health monitoring in the era of IoT. In this section, we introduce a hybrid intelligent medical architecture called EdgeCNN (see Figure 1) to solve existing problems. EdgeCNN realizes intelligent collaboration of edge and cloud. The architecture composes of IoT device layer, edge computing layer, cloud platform layer and third-party service layer.  
The IoT layer, which involves a series of health devices such as electrocardiographs, smart wristbands, smart watches, blood pressure meters and so on, is used to monitor various physiological indicators of human body and produce health data. Due to hardware limitations in computing power, storage and power, IoT devices do not have the ability to efficiently process these data [30]. Users need to upload data to the cloud or carry data to the hospital to obtain health analysis results. However, both methods are not real-time and convenient, thus cannot give early warning for potential health problems.  
The edge layer run the computing tasks on the resources close to the data source, so as to effectively reduce the delay of the computing system, reduce the data transmission bandwidth, and protect the data security and privacy. The edge layer is responsible for the execution of edge AI reasoning, executing the AI model distributed from the cloud, and feeding back the execution results to the user or cloud. In our architecture, the IoT monitoring device transmits the monitored data to an edge device through a local area network (e.g., WLAN, BlueTooth, and ZigBee), where a deep learning model for processing the corresponding data is deployed for diagnosis. Note that the deep learning model for edge computing needs to be re-designed, to make a trade-off between the accuracy of the diagnosis and the complexity of the model [31] so that it can run efficiently at the edge devices. By introducing the edge computing node, the data are transmitted only in the user-controllable local area network, thus protecting user's data privacy.  
The cloud platform layer provides global, non real-time, long-term big data processing and analysis. It carries out centralized AI model training according to business requirements, historical data, real-time data and AI execution feedback and sends the AI model to edge nodes for execution. In the framework of cloud and edge integration, it is very important to study the interaction between clouds and edge. Although users can obtain efficient health monitoring and diagnosis using only IoT devices and edge computing devices, they can still upload a part of data to the cloud platform for  
Figure 1: The edge cloud intelligent medical architecture.  
more accurate inference. As mentioned above, the deep learning model deployed on the edge device is a trade off between accuracy and complexity.  
The top layer of the architecture is the third-party service. Users can authorize relevant hospitals or private doctors to obtain their own data through cloud platform for further detailed diagnosis. The cloud platform can intelligently connect patients, medical staff, medical service providers and insurance companies in a seamless and collaborative way, so that patients can experience one-stop nursing, medical and insurance services.