Designing Mobile and Wearable Sensor-Fused Conversational Agents for Health and Wellbeing

Abstract

Mobile and wearable devices increasingly collect continuous wellbeing data, including sleep, activity, heart rate, stress, blood glucose, and blood pressure. Yet access to such data does not automatically help people interpret their condition or change behavior. Many health applications remain dashboard-first, presenting charts, thresholds, goals, and alerts while leaving users to decide what a change means and what action should follow. Conversely, generic LLM-based conversational agents (CAs) can provide fluent advice, but without personal sensor grounding, they cannot detect individualized patterns or provide contextual guidance. This three-hour tutorial teaches participants how to move from passive monitoring to actionable wellbeing dialogue. Participants examine a dashboard that combines wearable health-data visualization with conversational-agent feedback, then use Wearable Sensor-Dialogue Wellbeing Agent Studio (WSDWAS) to simulate wearables, generate sensor snapshots, configure agent personas and prompt blocks, and compare dialogue styles. Grounded in Positive Computing, the tutorial emphasizes autonomy, competence, privacy, safety, and boundaries between wellbeing support and medical advice.