Self-Interfaces : utilizing real-time biofeedback in the wild to elicit subconscious behavior change

Abstract

In this thesis, I introduce Self-Interfaces as a method for creating behavior change. Self-Interfaces are interfaces that intuitively communicate relevant aspects of covert physiological signals through biofeedback to give the user insight into their behavior and assist them in creating behavior change. The human heartbeat is a good example of an intuitive and relevant haptic biofeedback; it does not distract and is only felt when the heart beats fast. My vision is to identify other covert physiological processes and instances in which they become useful, and augment our awareness of those signals in order to create behavior change. As a first case-study, I develop the Self-Interface for Electrodermal Activity (EDA), which is designed to help regulate attention and interest in users with Attention Deficit Hyperactivity Disorder (ADHD). EDA is a covert physiological signal correlated with high and low arousal affective states. Three studies were carried out to: 1. identify the design criteria for development of the EDA Self-Interface, 2. identify guidelines to reduce the cognitive load imposed by the haptic biofeedback signal, and 3. identify the aspects of the EDA that are relevant and insightful for the ADHD population. The insights from these studies contributed to the design and development of the EDA Self-Interface which has three components: EDA Sensor (Affectiva E4 Sensor), a wearable haptic biofeedback interface, and a phone app to process the EDA data and communicate it with the wearable interface. Lastly, I discuss the evaluation criteria for the EDA Self-Interface and propose a longitudinal study for such evaluation.