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The Research of Structure and Algorithm for the Moving Evaluating System Based on Physiological Info

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Tutor: WangHongRui
School: Hebei University
Course: Detection Technology and Automation
Keywords: Physiological information fusion,Distributed Kalman filter,Fuzzy Neural Network,
CLC: TP202
Type: Master's thesis
Year:  2009
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In recent years, with the diversified development of information and computer technology , microelectronic technology and multi- sensor networks become more sophisticated, information fusion as an effective integrated approach , its theoretical and applied research has been rapid development . Information fusion has been widely applied to target identification , fault diagnosis and situation assessment , battlefield surveillance , automatic vehicle navigation , robotics , remote sensing , medical diagnostics , image processing , pattern recognition and complex industrial process control and other fields . , Information fusion technology applications in the field of physiological information in its infancy , most of the research focused on the qualitative aspects of medical diagnostic dynamic physiological information fusion research is still relatively small . With the development of information technology , the dynamic physiological information fusion is bound to be more and more attention , especially in sports health , sports and military training as well as astronauts in space simulation training and other areas . Fusion analysis of a number of physiological information can be obtained on the assessment of human health , but also through the integration of processing and monitoring of physiological information of a particular state , during exercise or training effect evaluation . However, due to the weak signal of physiological information , noise , nonstationary general characteristics of the physiological information fusion has a certain degree of particularity . This article is from a practical point of view, combined with information fusion theory , proposed assessment system based on the physiological information fusion movement model , and from the structure and function of the model were analyzed and explained . On this basis , the analysis demonstrated integration of a variety of advantages and disadvantages of the algorithm , and ultimately determine the core fusion algorithm based on to distributed Kalman Filtering and fuzzy BP neural network , the network of physiological information . Affiliated Hospital of Hebei University medical movement platform , carried the state of motion of the body's physiological indices such as blood pressure, pulse , heart rate and blood oxygen saturation data collection, the use of the GUI simulation tools simulation example . Simulation results verify the effectiveness and feasibility of the algorithm .
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