Jocelyn Scheirer, Rosalind Picard, and Aubrey Cantrell. 2025. Personalized Animations for Affective Feedback: Generative AI Helps to Visualize Skin Conductance. In Proceedings of the 3rd International Workshop on Multimodal and Responsible Affective Computing (MRAC '25). Association for Computing Machinery, New York, NY, USA, 146–151. https://doi.org/10.1145/3746270.3760237

Abstract

Biofeedback interfaces traditionally rely on abstract visualizations, tones, or haptics to convey physiological states—but these often lack personal relevance, emotional salience, and engagement. In this paper, we present a novel system that bridges wearable sensing and generative AI to create real-time, personalized animated biofeedback experiences. Users describe emotionally meaningful objects or scenes to a language model in our system, which outputs generate customized Processing animations. These animations are then dynamically driven by electrodermal activity (EDA) signals from a sensor worn on the wrist. We co-design and evaluate the system with autistic adults, many of whom have unique ''special interests'' that are likely to engage them more than a one-sized-fits-all visualization. Many of these individuals also have difficulty with interoception -- feeling or sensing their own internal and physiological state changes. to scaffold interoceptive learning through bespoke visual metaphors, we built this tool to transform passive physiological monitoring into an interactive multimedia experience, where the visual representation of the body is authored by the user. We introduce a prompt-engineered GPT-based interface that streamlines code generation, sensor mapping, and iterative refinement, requiring no prior coding expertise. The technical pipeline we built includes signal filtering, dynamic parameter mapping, and natural language-based customization—delivering a real-time, visually immersive feedback loop. We report on initial case studies with 12 autistic adults using the system, which highlight both the expressive potential and individual variability of user responses, reinforcing the need for adaptable multimedia frameworks in health technologies. By merging real-time physiological data with generative animation and natural language interaction, this work expands the creative frontier of personalized affective biofeedback. We also address ethical challenges arising from using AI with physiological sensors.