Research On Multiple Physiological Parameters Real-time Data Monitoring Based On Storm Cluster

Remote multi-parameters telemonitoring system makes it possible to access healthcare for the chronic, the old and cardiovascular diseases patients anytime and anywhere.With the development of smart mobile terminals and wearable sensor technology, thetraditional hospital-centered remote monitoring system cannot meet the needs of familyand personal care. Therefore, it is important to deal with the networking,transmission,computing and storage of large real-time data for remote multiple physiological parametersmonitoring at present.A novel monitoring solutions based on Storm for real-time data is proposed to run theanalytical algorithm with relative high time complexity, which provide the more preciseanalysis capability. It mainly includes the following distinctive works accomplished:(1) Many problems exist in traditional multiple physiological parameters monitoringsystem, such as difficult to achieve wearable sensing and wireless networking caused bythe "multi-parameter integrated" design mode of various sensing and acquisition units inthe physical layer and the data link layer. In this paper，a novel solution based on wirelessbody area sensor networks is proposed to meet the needs of multiple physiologicalparameters sensing, acquisition and networking in wearable and independent model. Hence,some problems of classical monitoring system can be avoided such as too many lead wires,inconvenient option, and limit range of movement.(2) In traditional remote multi-parameters telemonitoring system, the commonproblems are the insufficient data analyzing capabilities, limited storage space, datainconsistency and poor performance of real-time processing. The cluster monitoring basedon Storm platform for multiple physiological parameters is presented, meanwhileestablished the data monitoring center, which including setting up, design of data flow andoptimization for cluster.(3) The relatively difficult data analysis algorithms were studied for monitoring, suchas Wavelet and Support Vector Machine, which used to analyzed ECG and Pulse Wavesignals in this paper. With the rich data processing functions in Matlab, the highperformance of real-time data analysis can be implemented, which the classical systemcannot complete.As the results showed, the multiple physiological parameters monitoring system basedon Storm is much more outstanding in data processing and load balancing. The problems, such as more time needed in transmission, larger deviation in real-time analysis anddifficult to expanding, etc, in remote telemedicine services was solved, which providetechnical support for convenient home health care and facilitate disease warning servicesfor sub-health population efficiently.