Emotion and Stress Recognition Utilizing Galvanic Skin Response and Wearable Technology: A Real-time Approach for Mental Health Care

In modern society, people are exposed to various stressors and negative emotions daily and they may cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, this paper delves into the potential of modern wearable technologies as a tool for real-time health monitoring. The advent of ubiquitous sensing has ushered in an era where physiological and behavioral measurements can be continuously recorded in daily life. One significant physiological marker is the Galvanic Skin Response (GSR), which exhibits noteworthy changes under different emotional states. We propose a machine learning-based emotion recognition framework. It includes a preprocessing stage that eliminates noise and extracts 87 features from the GSR data. To account for individual differences in physiological responses, we also introduce a novel normalization procedure per subject. Finally, a subset of dominant and discriminative features enhances the proposed framework’s performance. We conducted experiments on two datasets, the wearable stress and affect detection dataset (WESAD) for stress detection, and the multimodal MAHNOB-HCI dataset for emotion recognition. The results show that the Leave-One-Out method is capable of detecting stress with 97.03% accuracy. Moreover, the proposed method classifies arousal and valence with an accuracy of 82.20% and 82.57%, respectively.