Message Classification Based Continuous Data Transmission for an E-health Embedded System

Abstract: This thesis aims to develop an e-health embedded system with a real-time operating system (RTOS), which allows users to monitor their body condition, including heart rate and breath, through Bluetooth Low Energy (BLE). Meanwhile, the device is also able to provide guidance for breathing by simulating breathing according to given parameters. In practice, the system samples the heart rate every two milliseconds. To ensure reliability and validity, results are expected to be sent in realtime. However, numerous data cannot be transmitted directly without being processed. Otherwise, the system will crash, and hard faults will occur. A general idea to solve this problem is to classify messages into two categories based on the priority. One is urgent, and the other is unimportant. Two solutions are proposed, one using a unidirectional linked list, and the second using queues. Based on an ARM micro-controller, the e-health embedded system is designed and implemented successfully. The evaluation results show that the solution using a linked list is suitable for the system, while the solution using queues is unable to solve the problem. With the help of the message classification, the urgent messages can be timely transmitted with continuous data.