An Optimization Method Using Clustering Technique for the Human Emotions Detection Artificial Neuro-Fuzzy Logic System

This paper utilizes clustering tool in MATLAB to find an optimal set of input parameters for the detection of human emotions using a neuro-fuzzy logic system. Previous studies have relied on a total of 14 physiological factors to detect one or more of 22 different human emotions. In this paper, we use clustering techniques to rank the factors in terms of their significance and impact on the system, and thus find a smaller subset of the factors for the detection of emotions. The clustering method shows that the stroke volume factor (SV) has the lowest impact in the model and as such can be eliminated from the set of factors. The electroencephalography (EEG), heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) are shown to have the highest impact on the model, and must be include in the input set of the model. We compare the clustering method with exhaustive methods for finding the optimal set of factors.