A MODIFIED FUZZY SUPPORT VECTOR MACHINE CLASSIFICATION-BASED APPROACH FOR EMOTIONAL RECOGNITION USING PHYSIOLOGICAL SIGNALS

Emotional state recognition has become an essential topic for human–robot interaction researches that diverted and covers a wide range of topics. By specifying emotional expressions, robots can identify the significant variables of human behavior and apply them to communicate in a very human-like fashion and develop interaction possibilities. The multimodality and spontaneity nature of human emotions make them hard to be recognized by robots. Each modality has its advantages and limitations, which, along with the unstructured behavior of spontaneous facial expressions, make several challenges for the proposed approaches in the literature. The most important of these approaches is based on a combination of explicit feature extraction methods and manual modality. This paper proposes a modified fuzzy support vector machine (FSVM) classification-based approach for emotional recognition using physiological signals. The main contribution of this study includes applying various data extraction indices and proper kernels for the FSVM classification method and evaluating the signal's richness in experimental tests. The developed emotional recognition method is also compared with conventional SVM and other existing state-of-the-art emotional recognition algorithms. The comparison results show an improved accuracy of the developed method over other approaches.