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Emotion recognition method based on adaptive fuzzy support vector machine

A support vector machine and self-adaptive fuzzy technology, applied in application, diagnostic recording/measurement, medical science, etc., can solve problems such as emotion classification and recognition information uncertainty limit, emotional physiological signal overlap, signal noise pollution, etc., to achieve The effect of obvious characteristics, improving the form of expression, and promoting the recognition rate

Inactive Publication Date: 2018-07-24
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0007] However, there are many ambiguities and uncertainties in the physiological signals extracted by machines, such as noise pollution caused by the interference of the surrounding environment when the signal is acquired, and the overlapping of various emotional physiological signals, etc.
Therefore, emotion classification and recognition is faced with the problem of information uncertainty and unclear boundaries.

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  • Emotion recognition method based on adaptive fuzzy support vector machine
  • Emotion recognition method based on adaptive fuzzy support vector machine
  • Emotion recognition method based on adaptive fuzzy support vector machine

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Embodiment Construction

[0043] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0044] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0045] like figure 1 As shown, the adaptive fuzzy support vector machine (Adaptive Fuzzy Support Vector Machine, AFSVM) is through the introduction of adaptive fuzzy membership μ i Indicates the different likelihoods that a sample belongs to the class. For sample set: (l is the number of samples) and kernel function K(x i ,x j ), where: category label y i ∈ {-1, +1}, degree of membership μ i ∈(0,1], the AFSVM optimization problem and constraints are as follows:

[0046]

[0047] s.t.y i [(w·x)+b]≥1-ξ i ,ξ i ≥0(i=1,2,...,l)

[0048] Among them, w is the weight vector, b is the bias, ξ i is the slack variable, and C is the penalty factor. Transform the above formula into its dual problem:

[0049] ...

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Abstract

The invention discloses an emotion recognition method based on an adaptive fuzzy support vector machine. The machine is used for the recognition of three target emotions of frustration, excitement andboredom of patients in the process of robot assisted rehabilitation training. The specific implementation includes the following steps of firstly, obtaining a physiological response signal of targetemotional response of the patients during the rehabilitation training; secondly, using a principal component analysis method to screen out an important characteristic set of the physiological signal indicating target emotional changes; finally, based on the characteristics that different physiological signals are easily disturbed by noise and the physiological signals among different emotions often overlap one another, the emotion recognition method based on the adaptive fuzzy support vector machine is proposed. The method not only improves the representation form of a membership function, butalso defines parameters which controls critical membership degree and an attenuation trend of the membership degree, so that the membership degrees can be adaptively adjusted according to the specific distribution characteristics of different emotional physiological signals.

Description

technical field [0001] The invention relates to signal processing and pattern recognition, in particular to an emotion recognition method based on an adaptive fuzzy support vector machine. Background technique [0002] Emotion is an important instinct of human beings. Like human voice and body shape, it plays an important role in people's daily life, work, communication and transaction processing. Emotion recognition using computer technology is the key technology to realize advanced human-computer interaction, and it is of great significance to realize human-computer interaction, human-computer interface and intelligent computer. Emotion recognition is mainly realized through facial expressions, speech, human body posture and physiological signals. Research based on extrinsic signals such as facial expressions and speech has a long history, but the results are often subjective. The physiological changes of the human body are mainly controlled by the autonomic nervous syst...

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Application Information

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IPC IPC(8): A61B5/16A61B5/0476A61B5/0488A61B5/0402A61B5/0205
CPCA61B5/0205A61B5/16A61B5/7267A61B5/318A61B5/369A61B5/389
Inventor 徐国政陈雯黄国健高翔冯琳琳陈金阳
Owner NANJING UNIV OF POSTS & TELECOMM
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