Taboo searching method for selection of galvanic skin response signal features

Abstract

The invention discloses a taboo searching method for selection of galvanic skin response signal features, which includes the following steps: sequence backward algorithm is adopted to form N-1 rows and a two-dimensional table L of N lines, wherein the N represents total number of dimensions of selected features, each line represents one feature, each row is called one space, an nth space selects n features, and n is larger than or equal to 1 and less than or equal to N-1; a value of each element in the table is expressed by 0 or 1, 0 represents that the element is not selected when feature selection is performed, and 1 represents that the element is selected; the selected features in each space are solved by adopting taboo searching algorithm to obtain a table S formed by solution of each space; and a feature with the largest fitness function in each space serves as a final feature selection result. The taboo searching method for the selection of galvanic skin response signal features not only obtains effective features of skin electric signals with large contribution to emotion recognition, but also overcomes the shortcoming that a basic taboo searching algorithm is easy to get into local optimum.

Description

technical field [0001] The invention relates to a physiological signal emotion recognition method, in particular to feature selection of electrodermal response signals. Background technique [0002] Emotion recognition is an important part of the field of affective computing. If a computer is to have emotions, it must first be able to recognize emotions. Emotion recognition is particularly important. Emotion recognition (Emotion Recognition) is to infer the emotional state by observing the premise environment of human expression, behavior and emotion. Only when emotion recognition is regarded as a pattern recognition problem and emotion expression is regarded as a pattern synthesis problem, it is feasible for computers to carry out emotional communication. The objects of emotion recognition research are mainly five kinds of facial expressions, voice intonation, human body posture, text (commonly used questionnaire survey method in psychology) and physiological signals. The...

AI Technical Summary

Problems solved by technology

Due to the differences in the physiological signals and emotion types used by each researcher, the recognition results are different

Among the physiological signals used by researchers, there are GSR signals. The GSR features they extract are usually several statistical features such as first-order difference and mean value in the time domain, but in the end they did not summarize which specific features are very important for recognizing emotions. big contribution

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