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

2025-08-05

AI Summary

To address the bottlenecks of clinical-grade equipment dependency and labor-intensive continuous labeling in home health monitoring, this paper proposes a real-time anomaly detection framework integrating wearable devices and ambient intelligence. Methodologically, it leverages the UniTS unified time-series model to perform personalized continual learning over heterogeneous multi-source sensor data—without requiring manual annotations—and incorporates large language models (LLMs) to enhance clinical interpretability of anomalies while enabling cross-platform deployment across medical- and consumer-grade devices. Our key contributions are: (1) the first end-to-end, non-invasive, lightweight-device-driven framework for anomaly detection and semantic interpretation in home settings; and (2) empirical validation showing a 22% average F1-score improvement over 12 state-of-the-art methods in real-world home environments, demonstrating the feasibility of high-quality, personalized early warning using low-barrier hardware.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Detects real-time health anomalies using wearable and ambient AI
• Learns individual patient patterns to identify subtle health risks
• Improves anomaly detection accuracy without clinical-grade equipment

Innovation

• Fuses wearable sensors and ambient intelligence
• Uses UniTS for personalized anomaly detection
• Integrates LLMs for interpretable health insights