Emotion-aware systems are emerging as a frontier in interactive media design, promising adaptive, personalized experiences based on the user’s internal state. This doctoral research investigates how peripheral physiological signals—such as heart rate (HR) and elec- trodermal activity (EDA)—captured through wearable sensors can be used to infer emotional states in real time and drive dynamic adaptations in media content and interface design. While affective computing has made substantial progress in emotion recognition, most systems rely on static user profiles or intrusive biosensing (e.g., EEG), limiting real-world applicability. My research proposes a lightweight, real-time emotion-adaptive framework that uses pe- ripheral physiological data to support emotionally intelligent design systems. This work sits at the intersection of affective computing, adaptive systems, and computational design, with the goal of mov- ing beyond static personalization toward continuous emotional responsiveness and user-centric experience modulation.