| The mobile medical monitoring system based on Internet of things is a hot topic in recent years. It is based on radio frequency identification technology and wireless network technology as the core. Through the medical information management system or mobile handheld terminal equipment for patient monitoring, simultaneously to medical personnel to provide assistance information service system. Use of the Internet of things of mobile medical monitoring system of rapid, accurate, convenient auxiliary medical personnel capture patients physiological state to prevent the happening of the disease has extremely important significance.This topic research of mobile medical monitoring system based on Internet of things has realized the geographic location of the track of the patient, at the same time to test the patient’s ECG characteristics. According to the characteristic parameters of ECG waveform analysis diagnosis, to realize remote monitoring of patients.This paper studies the main content including GPS technology, the ECG preprocessing technology, ECG waveform detection technology and ECG diagnosis technology. Mobile monitoring terminal for testing.Firstly, GPS positioning information is an important message of iot information collection. Analysis of the composition and principle of the GPS positioning technology and Then analysis of the composition and principle of the GPS positioning technique and coordinate transformation. Using the Android platform and Google API design software, Real-time locating and tracking in patients with heart disease. When there is a sudden emergencies, the position information is sent to the monitoring center in time. Test results show that for mobile monitoring terminal. GPS’s accuracy of5to30meters in the empty place. GPS’s accuracy of10to80meters in the distribution of complex buildings. Locate the execution time is about15seconds.Second, This article is based on Internet of things of mobile medical monitoring system of biological information acquisition mainly to realize the collection of ECG signal through wireless bluetooth. Analyzing the characteristics of the noise introduced in the process of signal acquisition. According to baseline drift, power frequency interference and myoelectricity interference of interference signal source, respectively using median filter. comb filter, Butterworth filter to remove noise. At the same time using the MIT-BIH database to evaluate algorithm of filtering effect. Experimental results show that the filtering algorithm can filter out interference very well and good real-time performance. This is advantageous to the ECG data post-processing.Third, in ECG characteristic waveform detection mainly research the QRS wave, P wave and T wave, and to extract the waveform characteristics of starting point and end point. In this paper, Using adaptive threshold difference algorithm for detecting QRS wave. Comparative analysis of the traditional fixed threshold difference algorithm and adaptive threshold difference algorithm, Experiments prove that the adaptive threshold differential algorithm of detecting effect is better, use improved local transformation method to detect P.T wave. Using the database to evaluate detection algorithm to evaluate detection algorithm. QRS wave group detection accuracy rate is99.71%, the P and T wave detection accuracy rate is91.05%. The test results show that the algorithm can improve the detection accuracy of each wave and suitable for practical application. The experimental results showed that two kinds of algorithm precision is high and good real-time. This is advantageous to process a variety of information.Finally, Using the above detection algorithm obtained the features of waveform parameters. Using these parameters automatically analyze the patient’s heart state programs. Realize the Internet of things in the critically ill alarm function. At the same time the data sent to the monitoring center for further analysis through the GPRS network. Realize information interconnection. |
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