VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data

Researchers introduce VitalAgent, an AI framework that combines language models with tool-augmented reasoning to enable both reactive question answering and proactive monitoring of physiological data from wearable devices like ECG and PPG sensors. The framework achieves 30% improvement over baseline approaches and is validated against a new benchmark dataset (VitalBench) containing 1,862 QA pairs and 90+ hours of continuous biometric recordings.

VitalAgent addresses a significant gap in current mHealth technology by moving beyond static, task-specific prediction systems to support dynamic temporal reasoning over continuous physiological streams. Traditional wearable health systems operate in isolated prediction pipelines or respond to static queries, lacking the capability to maintain persistent context across long-term signal monitoring or proactively detect anomalies. This research demonstrates that augmenting large language models with specialized tools for signal processing and temporal analysis creates more sophisticated health monitoring systems.

The framework's architecture leverages a longitudinal physiological memory system paired with dynamic computation tools, enabling the model to reason about complex patterns in ECG and PPG data without requiring manual feature engineering. The introduction of VitalBench as a comprehensive evaluation dataset with both reactive QA tasks and extended monitoring scenarios establishes a standardized approach to benchmarking mHealth AI systems—a critical need as wearable adoption accelerates.

For the digital health industry, VitalAgent's 30% performance improvement over existing baselines suggests meaningful accuracy gains that could translate to better clinical outcomes and user experiences. The framework's ability to support proactive alerting over extended signal streams addresses a key requirement for remote patient monitoring and chronic disease management applications. Healthcare organizations and wearable manufacturers could leverage this approach to build more intelligent, context-aware health systems that maintain understanding of individual physiological baselines and detect meaningful deviations.

• →VitalAgent achieves over 30% improvement compared to prompt-based and ReAct baselines through tool-augmented reasoning over physiological signals.
• →The framework supports both reactive question answering and proactive monitoring, addressing limitations of existing task-specific mHealth pipelines.
• →VitalBench dataset provides standardized evaluation benchmarks with 1,862 QA pairs and 90.2 hours of continuous ECG/PPG recordings.
• →Longitudinal physiological memory enables persistent context across long-term signal streams, improving temporal reasoning capabilities.
• →Dynamic tool use for raw signal computation eliminates manual feature engineering and adapts to individual baseline variations.