This article presents a comprehensive technical framework for an artificial intelligence pipeline designed to detect critical health events from wearable device data in real-time. The system focuses on two high-priority health concerns: fall detection using accelerometer and gyroscope data, and cardiac arrhythmia identification through electrocardiogram (ECG) signals. By integrating specialized deep learning models with streaming data architecture, the pipeline enables prompt detection and notification of potential emergencies to caregivers or medical professionals. The framework consists of four main components: a data acquisition layer that interfaces with wearable sensors, a streaming infrastructure built on Apache Kafka and Spark Streaming, an AI processing engine applying hybrid CNN-LSTM models for fall detection and specialized CNN architectures for arrhythmia classification, and an alert notification system delivering contextually rich information through multiple communication channels. The article details the preprocessing requirements, model architectures, streaming implementation, and deployment considerations including edge-cloud processing distribution, latency management, and privacy measures. Extensive evaluation using PhysioNet datasets demonstrates the system’s effectiveness in distinguishing health events from normal activities with high accuracy and minimal latency, making it suitable for clinical applications requiring timely intervention. The proposed architecture balances immediacy of detection with analytical depth, providing a scalable foundation for preventative healthcare monitoring that respects user privacy while enabling potentially life-saving notifications.
Keywords: arrhythmia classification, fall detection, real-time event detection, stream processing, wearable health monitoring
