面向健康与福祉的移动及可穿戴传感器融合对话智能体设计
Designing Mobile and Wearable Sensor-Fused Conversational Agents for Health and Wellbeing
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中文总结 AI 辅助
本教程面向健康领域，旨在教授参与者使用WSDWAS工具，将可穿戴传感器数据与LLM驱动的对话智能体结合，实现从被动监测到可操作福祉对话的转变。
中文摘要 AI 辅助
移动设备与可穿戴设备日益收集睡眠、活动、心率、压力、血糖、血压等连续的福祉数据，但获取此类数据并不能自动帮助人们解读自身状况或改变行为。许多健康应用仍以仪表盘为核心，呈现图表、阈值、目标与警报，却让用户自行判断变化的含义及后续行动。相反，通用的基于大语言模型（LLM）的对话智能体（CAs）虽能提供流畅建议，却因缺乏个人传感器数据支撑，无法检测个性化模式或提供情境化指导。本三小时教程教授参与者如何从被动监测转向可操作的福祉对话。参与者将研究结合可穿戴健康数据可视化与对话智能体反馈的仪表盘，随后使用可穿戴传感器-对话福祉智能体工作室（WSDWAS）模拟可穿戴设备、生成传感器快照、配置智能体角色与提示块，并对比对话风格。该教程以积极计算为基础，强调自主性、能力、隐私、安全，以及福祉支持与医疗建议之间的界限。
英文摘要
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.
