An Emotion Recognition Method Based On TM_EMD Algorithm And Multi-modal Characteristics Of Pulse Signal

Emotion research as a comprehensive topic involving many different fields of knowledge has attracted widely attention in Psychology, Computer science and Medical field. It has the highly integration and practical value. Emotion research based on physiological signals is a novel topic in the field of Psychology and Medical. Pulse as a common physiological feature reflects the arterial pressure changes under the interaction of heart and arterial vascular system and also closed related to the health status. The difference character of pulse signals can be used to analyze and recognize vary emotion status, it is as a research can provide the basis of clinical medicine, social science and engineering application.This research is based on previous study, it puts forward a new emotion recognition method due to the deficiencies existing current emotion recognition method. It can recognize the different emotion states of individual by the extracting the spectrum character, nonlinear character and empirical mode decomposition character. It proves to be that this method has the higher recognition rate. More details as below:(1) It analyzes the feasibility of pulse signal-emotion recognition research in details according to the physiological mechanism generated by pulse signal. Pulse as a physiological feature can not be controlled subjectively and can reflect emotion objectively. In addition, pulse signals have the advantages of simple collecting and easy processing.(2) Analyzing wavelet conversion theory:remove the noise exist in the pulse signals by db5wavelet combing with the noise type in the acquisition process of pulse signals. Low-frequency noise from pulse signal processes by approximate component zero setting and high-frequency noise from pulse signal by improved threshold value and then rebuilds pulse signals by wavelet coefficients.(3) Empirical mode decomposition algorithm is introduced to the pulse signal emotion recognition study; it comes up with an improved method in the terms of endpoint effect in the process of signal decomposition. This method first takes use of signal coherent averaging technique to obtain the pulse signal template and then match the data located the endpoint with template extending original signal to the whole cycle; at last compose the extended signal in the empirical mode by Mirror extension method.(4) This study integrates EMD generated by Empirical mode decomposition and nonlinear and spectrum character of pulse signal and then apply to the pulse-emotion recognition research discussing different pulse signal reflected by different emotions。 Gained the extraction methods of three types of certain character information and released the extraction of pulse signal effective character.(5) This study also constructed Multi-classification based on supporting Vector Machine in order to fulfill auto-recognition of different emotion. Extract pulse signal from80samples(20samples of states of happiness,20samples of states of pleasure,20samples of states of sadness,20samples of states of anger) to do auto-recognition analysis and it proves that combination of pulse signal multi-mode character information is a more efficient way to emotion recognition.