Wearable Health Data Anomaly Detection Framework

Construct a sophisticated anomaly detection framework for processing continuous streams of wearable health sensor data. Design a system capable of real-time signal processing, adaptive threshold generation, distinguishing between normal physiological variations and genuine health anomalies, and generating contextually appropriate alerts. Include machine learning models for personalized baseline establishment and minimal false-positive generation.