Study On Remote Health Monitoring Technology Based On The Wearable Sensor

With the aging and empty-nest phenomena have become increasingly serious in our country, people began to pay more attention on the elderly health problems. This paper takes the remote-health-monitoring system as the background, and firstly studies the application value of the wearable sensors and the ZigBee technology in WSN. Secondly, this paper studies two characteristic parameters of the wearable sensors about its application on the remote-medical-monitoring system: the ECG signals and the Fall-Detection signals. Furthermore, the proposed algorithm proposed by this paper enhances the calculation accuracy and abates the time complexity.Considering the noise of the ECG signals and the power consumption problem of sensor nodes, this paper puts forward the ECG detection algorithm using wavelet and autocorrelation transform. The proposed algorithm apply the wavelet and autocorrelation transform to simplify the self-adaptive threshold setting in the peak detection algorithm and exclude the unstable R-R wave bands which affect the accuracy of the heart-rate calculation to obtain the more accurate values.Finally, in order to make more accurate judgment about the state of the monitoring object, this paper puts forward the fall-detection algorithm based on multiple signal characteristics of the Fall-Detection signals. In the end, with the data gathered from the shimmer nodes based on the Tiny-OS platform, the experimental results show that the improved algorithm proposed in this paper Conforms to the actual demand and has better calculation accuracy.