AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence

TL;DR

AI on the Pulse is a real-time health anomaly detection system that combines wearable sensors, ambient intelligence, and advanced AI to provide personalized, continuous patient monitoring and alerts, demonstrating robustness and interpretability in real-world settings.

Contribution

The paper introduces UniTS, a universal time-series model for personalized anomaly detection, and demonstrates its deployment on lightweight, non-invasive devices for continuous health monitoring.

Findings

Outperforms 12 state-of-the-art anomaly detection methods with ~22% F1 score improvement.

Successfully deployed in real-world @HOME setting for continuous patient monitoring.

Provides clinically meaningful insights through integration with large language models.

Abstract

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.