Remote physiological signals monitoring system design for rehabilitation training
Qian, Jiayan (2017-08-15)
Remote physiological signals monitoring system design for rehabilitation training
Qian, Jiayan
(15.08.2017)
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Turun yliopisto
Kuvaus
Siirretty Doriasta
Tiivistelmä
Rehabilitation training is a kind of therapies for limb dysfunction patients caused by stroke. Traditional rehabilitation training relies on Physician’s subjective judgment, lacking in accurate evaluation means for training process and effects. Therefore, physiological signals are applied to rehabilitation evaluation so as to assist rehabilitation training. In addition, it is of low efficiency and large limitation that physicians usually need to guide patients one to one and face to face. In order to help physician remotely guide patients do rehabilitation training, the thesis will design a remote physiological signal monitoring system for rehabilitation training. Main works are as follows
(1) Sensor node hardware device will be designed. According to the features that physiological signals are susceptible to interference and have small amplitude, high precision chip ADS1198 is adopted as the core of the analog front end. Taking miniaturization into consideration, RTX4140 Wi-Fi module integrated MCU is chosen to accomplish on-device functional programming and achieve wireless data transmission. Due to its high efficiency, no connection, and unidirectional transmission, UDP is selected as the system data transmission protocol.
(2) Back-end cloud server and front-end web application will be designed and built. Considering the capability of holding high data streaming, multi-clients and real-time communication, high-performance Node.js is used to implement back-end server, and WebSocket is chosen for real-time communication with browsers. UDP data interface is built with data validation, to prevent the impact of network packet loss. Ionic is adopted as the web application framework with PouchDB as the database solution, implementing user interface, curve chart, digital filter, signal analysis API, and other functions.
From related technology investigation and system architecture design to hardware/software module implementation, the thesis will finish a remote physiological signal monitoring system design for rehabilitation training. And finally the system integrity and functions will be tested.
(1) Sensor node hardware device will be designed. According to the features that physiological signals are susceptible to interference and have small amplitude, high precision chip ADS1198 is adopted as the core of the analog front end. Taking miniaturization into consideration, RTX4140 Wi-Fi module integrated MCU is chosen to accomplish on-device functional programming and achieve wireless data transmission. Due to its high efficiency, no connection, and unidirectional transmission, UDP is selected as the system data transmission protocol.
(2) Back-end cloud server and front-end web application will be designed and built. Considering the capability of holding high data streaming, multi-clients and real-time communication, high-performance Node.js is used to implement back-end server, and WebSocket is chosen for real-time communication with browsers. UDP data interface is built with data validation, to prevent the impact of network packet loss. Ionic is adopted as the web application framework with PouchDB as the database solution, implementing user interface, curve chart, digital filter, signal analysis API, and other functions.
From related technology investigation and system architecture design to hardware/software module implementation, the thesis will finish a remote physiological signal monitoring system design for rehabilitation training. And finally the system integrity and functions will be tested.