In our day-to-day life, the proper perception of emotion plays an important role in human decision making and behavior. Nowadays, a lot of research is focused on the evocation and precise detection of human emotion, which can be later utilized in a different set of arena. There is good amount of research on emotion detection through parameters extracted via Face Recognition and Speech Modulation, etc. However, there is a huge question on the accuracy or effectiveness of these results as these features can be controlled or manipulated by the subject/person. So, the next approach is the usage of Physiological Signals. These signals are generated by the Central Nervous System (CNS) and cannot be controlled or manipulated by the subject/person. In the proposed work, we have used Galvanic Skin Response (GSR) signals for emotion detection. It is an easily available off-the-shelf, non-invasive sensing device, and is easy to use. We have used different machine learning models to classify the various emotional states with better accuracy. The different classifiers that are used are the k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Logistic Regression (LR).

No abstract

No abstract

No abstract

No abstract