Design Of A Remote Pulse Signal Monitoring System Based On Wireless Technology On Smartphones

In recent years, the problem of population aging and shortage of medical resources become seriously. Home health care system becomes a popular research direction. Wireless communication technology of short-range reduces the cost and smart phones become popular. Human pulse signal contains a wealth of physiological and pathological information and have a close contact with cardiovascular diseases, so it is important to monitor human pulse signal. This paper combined Bluetooth 4.0 with android smart phones designing a monitoring system. The main contents in this paper are as follows.This design selects a suitable pulse acquisition sensors and signal preprocessing circuit, wireless transmission and mobile development platform.This paper builds a pulse measurement circuit, use a finger clip photoelectric sensor to capture human pulse signal. The collected signal performs current to voltage converter, amplification, filtering and 50 Hz notch treatment. The signal output to the smallest system which using CC2540F256 chip as a master chip. Pulse signal is captured up and transmissed to another bluetooth devices.This research designs a mobile phone client application in the Eclipse development environment with Java language on Android-based phone. The application is used to connect another Bluetooth device, receive data, calculate pulse rate, save data and review pulse rate. The app also can monitor the change in pulse rate, when it becomes abnormal, you android phone will send a text message to designated contacts, this is an alarm function. The pulse of the data stored in the mobile phone can be analysised by PC offline.Testing and analisising the pulse monitoring system, the result can verify that wireless data transmission and mobile applications having a good quality and reliability. The pulse signal is analysised in time domains and frequency domains. it selects the main wave slope R, 20% pulse signal width, and pulse diagram K value to process the data, and lastly using GA-SVM calculation to calculate the pulse signals and differentiate healthy individual and individual with arteriosclerosis. It gets the correct classification rate of 81.8% and verifys the effectiveness of the characteristics. provided some theories for the reference of cardiovascular diseases.