Galvanic skin response to recognizing human behaviour

Abstract

Human beings' health is affected by physical ventures and emotional states endured by regular activities, which frequently develop attitudes and substantially affect health outcomes. Human Emotions play a vital role in deciding perception, cognition, memory, attention, reasoning, and decision-making. Several approaches have been used for automatically recognizing users' sentiment through images, speech, text, video, and physiological signals. Truthful detection of human emotions and personality behaviours can be advantageous for many situations, like interviews, group discussions, polygraphs, depressed persons, paralytic patients, blind people, shooters, etc. So, there is a need for an emotion recognizer. Though researchers tried several methods for emotion recognition, the accuracy of detection is always a question. The main aim is to develop a precise classification model for better accuracy of the emotion recognition system. Therefore, an emotion detector using GSR (Grove – GSR Sensor V1.2) sensor is proposed in the current research work. Twenty pupil groups were subjected to under observation for six different human activities, viz., happy, relax, stress, pain, reading, and math calculation. This research work was carried out in the lab of machine vision (CEERI) Central Electronics Engineering Research Institute Pilani, Jhunjhunu India. Moving average window method was used for data pre-processing. Supervised machine learning models viz., k-nearest neighbours (KNN), support vector machine (SVM), and decision tree (DT) was used for emotion classification. The decision tree model gives the best results with an average accuracy of 97.61%. Pain activity is most correctly recognized with greater than 99% accuracy.