Fuzzy Logic Based Approach for Automation of Emotion Detection in Misophonia

Abstract

Human being mostly categories into two types, the one who follow distress cycle and other who believe on wellness cycle. Output of distress cycle is decreased productivity, decreased enjoyment and decreased intimacy, whereas output of wellness cycle is increased productivity, increased enjoyment and increased intimacy which is essential for life. Reason of distress could be anything like emotion, disease, environment, family, pain. One of the mostly unexplored areas of cause of distress is misophonia. It is a disorder related to various senses like sound, sight, smell, taste and touch. This disorder is not in DSM-V, underscoring that it is not known by psychiatric and medical communities. We have proposed an automatic fuzzy approach system for identification of intensity of emotions related to various triggers on the basis of age group.

Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning

“…However, convolution techniques have been used [ 21 ] for reading EEG signals but they have no flexibility of reading signals in time-frequency domain and sometimes because of nonstationary behavior of the EEG signal they need to compromise on accuracy [ 25 ]. There are unlimited areas where stress gets evoked and reason for it could be noise trigger or unpleasant vision [ 26 ]. As it is said every task is time bounded and it is proven in studying correlation of activity and time in [ 24 ].…”