Research And Implementation Of Sub-health Recognition Algorithm Based On BP Neural Network

With the increase of the pressure of life,the sub-health problem is more and more serious,which has seriously affected people's lives.At present,the domestic and international evaluation of sub-health is mainly a questionnaire survey,this method is subjective and time-consuming.In this thesis,a method of combining pulse signal with neural network is proposed to judge sub-health.First,the eigenvalue of pulse signal are extracted,and then calculated by BP neural network,to achieve the objective recognition of human sub-health status.The main work o,f this thesis is as follow:(1)Using MATLAB to realize preprocessing and eigenvalue extraction of pulse signal.These eigenvalues can be used to train and test BP neural networks with different structures.(2)On the ARM platform to achieve the serial pulse data reading and real-time display,and preprocess of the read pulse data and extraction of pulse signal eigenvalue.(3)The BP neural network is trained by the eigenvalue value,and the sub-health recognition application program is compiled on the QT platform.The classification and recognition of-sub-health pulse signal is realized by the calculation of the eigenvalue value with the BP neural network.(4)Finally,the recognition program of sub-health,is transplanted to the ARM platform.The sub-health pulse recognition system designed in this thesis has successfully extracted the eigenvalues of the pulse signal and realized the recognition of the sub-health state of the pulse signal.Experiments show that this method can be used to detect sub-health status.