VitalAgent实现可长期追踪的智能生理监测，支持主动预警与动态问答。
VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data
- 基于长期生理记忆与工具增强推理，动态处理原始信号
- 反应式任务准确率比基线提升25%以上，支持90.2小时连续监测
- 适合需要主动健康预警的可穿戴医疗场景
可穿戴设备可持续监测心电图（ECG）和光电容积脉搏波（PPG）等生理信号，但现有移动健康系统多局限于特定任务预测或对静态摘要的被动问答，缺乏时间推理能力、持久生理上下文建模及对长期信号流的主动监控。本文提出VitalAgent，一种基于ECG/PPG的工具增强型智能体框架，支持反应式问答与主动监测。该框架依托长期生理记忆与工具增强推理接口，实现对原始信号的动态计算。我们还构建了VitalBench，一个纵向生理监测基准数据集，包含1,862个问答对用于反应式任务，以及90.2小时连续的ECG/PPG记录用于主动监测，涵盖心脏、身体活动与压力相关任务。实验表明，VitalAgent在反应式评估中相比提示工程与ReAct基线提升超过25%，并能实现对长期生理信号的主动告警监控，凸显动态工具使用与长期生理监测的重要性。
原文摘要 · Abstract (English)
Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines or reactive question answering over static summaries. They lack the ability to support temporal reasoning, persistent physiological context, and proactive monitoring over long-term signal streams. We propose VitalAgent, a tool-augmented agentic framework for ECG/PPG-based mHealth that supports both reactive question answering and proactive monitoring. VitalAgent is built on a longitudinal physiological memory and a tool-augmented reasoning interface that enables dynamic computation over raw signals. We further introduce VitalBench, a longitudinal physiological monitoring benchmark dataset comprising 1,862 QA pairs for reactive question answering and 90.2 hours of continuous ECG/PPG recordings for proactive monitoring, covering cardiac, physical activity, and stress-related tasks. Experiments demonstrate that VitalAgent achieves over 25% improvement over prompt-based and ReAct baselines in reactive evaluation and supports proactive alert monitoring over long-term physiological signals, highlighting the importance of dynamic tool use and long-term physiological monitoring.
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