Research Of Compression Algorithm On Health Monitoring Data And Its Application In Health Monitoring System

With the development of Internet of things and wireless sensor network technology,intelligent human health monitoring equipment has been widely concerned and popular.It is very necessary to monitor and analyze the risk of cardiovascular and cerebrovascular diseases in the elderly by using the human intelligence monitoring equipment.In addition,because the wireless sensor network has characteristics of limited bandwidth and energy,large amount of data transmission and high redundancy,in order to reduce the energy consumption of data storage and transmission process,the compression processing of the monitoring data has great practical value.According to the different characteristics of monitoring data,two kinds of data compression algorithms were proposed.The specific implementation of the algorithm and simulation results were described in detail and comparative analysis.At the same time,this paper designed and implemented a intelligent health monitoring system,from the hardware,software and the main principles of each function module were related to the introduction.Finally,the compression algorithm was applied to the system,which proveed the application value of the algorithm.Specific work is as follows:1.According to the respiratory signal of the monitoring,which is generally more stable and has large time correlation,a data compression algorithm based on piecewise linear fitting was proposed.The core idea of the algorithm is adaptive threshold judgment and piecewise linear fitting,adaptive threshold value is to test the rationality of the adjusted model by adjusting the error before and after the fitting;piecewise linear fitting is to determine a reasonable interval through the division of discontinuous point fitting,and the simulation results showed that,when the compression ratio is in the approximate,the algorithm in this paper has higher compression accuracy,and has better compression effect for mutation signal;2.According to the the heart rate signal monitored by the system,which has large fluctuation and complex frequency components,a data compression algorithm based on wavelet transform was proposed.At first,the algorithm used integer lifting wavelet transform based on basis function with bior2.2,then the wavelet coefficients were divided into two categories by K-means classification method,the coefficient in small categories were treated as zero,finally,the coefficients was coded by the adaptive Huffman coding algorithm which has fixed code length.Simulation experimental results showed that under the same coefficient threshold algorithm in this paper has smaller data compression ratio and higher signal reconstruction accuracy,which means it has better compression ability and good data recovery capability;3.Design and implement of the intelligent health monitoring system.The system mainly consists of signal acquisition module and data analysis and processing module,it also supports data display in real time,the design block diagram of overall structure of the system and the specific functions and structure of each module was explained,finally,a volunteer was invited to carry out the test,and the monitoring effect chart was analyzed and explained;4.The compression algorithm was applied to the intelligent health monitoring system.The energy consumption and data transmission time of the system were compared.The results showed that the system energy consumption is reduced by 60% and transmission time is reduced by 70%,at last,the function test of the system was carried out.