用可穿戴设备和AI实时监测健康异常，无需频繁标注。
AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence
- 融合可穿戴设备与环境感知，用AI学习个体生理规律。
- 在真实场景中比12种顶尖方法提升约22%的检测准确率。
- 适合居家慢病管理，且能生成医生可读的异常解释。
我们提出AI on the Pulse，一个面向现实世界的异常检测系统，通过融合可穿戴传感器、环境智能与先进AI模型，持续监测患者健康状态。系统基于当前最先进的通用时间序列模型UniTS，自主学习每位患者的独特生理与行为模式，识别可能预示健康风险的细微偏离。相比需持续人工标注的分类方法，本方案采用异常检测实现无标签、实时的个性化预警，支持居家干预。实验表明，该方法在高保真医疗设备（如心电图）与消费级可穿戴设备上均表现优异，相较12种主流异常检测方法，F1分数提升约22%。真正的价值体现在@HOME部署中，系统使用轻量非侵入式设备（如智能手表）实现全天候真实世界监测，证明高质量健康监控无需临床级设备。此外，通过集成大语言模型，将异常评分转化为临床可理解的洞察，提升可解释性。
原文摘要 · Abstract (English)
We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~ 22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.
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