Lightweight 1D CNN model for emotion classification using GSR signal

Emotions can be detected through facial expressions, voice signals, and physiological signals such as Galvanic Skin Response (GSR), Electroencephalogram (EEG), and Electrocardiogram (ECG). However, there have been limited studies on using GSR signals for emotion detection. Emotion recognition systems (ERS) use artificial intelligence to diagnose emotions and trigger appropriate actions accurately. This study introduces a novel lightweight deep Convolutional Neural Network (LWDCNN) for emotion classification using GSR data. The LWDCNN model reduces computational complexity while improving the speed and accuracy of the classification. GSR data is normalized based on suitable segmentation. A seven-fold cross-validation technique is employed to classify emotions using LWDCNN based on the arousal and valence dimensions. Emotions are classified based on the social and non-social context of the subjects using GSR data from the AMIGOS (A Dataset for Affect, Personality, and Mood Research on Individuals and Groups) database in both individual (non-social) and group (social) settings. The accuracy of valence classification is 78.83% for individual settings and 79.96% for group settings. The accuracy of arousal classification is 80.62% for individual settings and 84.42% for group settings. This model can effectively be used to diagnose the subjects' mental health by detecting the appropriate emotions. Goa College of Engineering, Affiliated with Goa University, Goa, India.