Abstract:
Stress has become one of the main contributors to serious mental and physical health issues in today’s world. Existing works in the literature have used Psychophysi ological measures and proposed numerous mechanisms to detect stress and administer feedback to help users regulate it. Unobtrusive wearables’ popularity is increasingly growing, intertwined with digital health notions, making them efficient, inexpensive, and easily accessible affective self-help technologies. This thesis first aims to investigate and implement stress detection mechanisms in the laboratory and everyday environ ments using unobtrusive wearable devices. In this regard, we investigate various sce narios, such as how to design and deploy stress measurement models that can efficiently use multi-modal data coming from different types of wearables used in the laboratory and real- life settings. We also study low-cost and practical methods for emotion regula tion in stressful conditions of everyday life. In the next step, a mixed-methods study is conducted. For this, signals from multiple wearables and users’ subjective opinions re garding different aspects of wearability were analyzed quantitatively and qualitatively. The next step is an in-depth study in cooperation with HCI researchers, in which we demonstrate the effects of haptic feedback on emotion regulation. As a next step for helping users choose the right device, we evaluate several wearables under completely identical conditions to compare the stress detection quality in wearables with differ ent technologies. Finally, we utilize Explainable AI (XAI) to make our models more understandable for the end users, and in particular for the psychology and clinical experts. The results of our studies indicate that an integrated detection, notification, and intervention cycle is required to ensure a reliable system for regulating stress in daily life.
