⚜️Real-Time Analysis

Real-Time Data Stream Processing

Integrating Apache Kafka and Flink to enable real-time data ingestion and analysis.

Example: Real-Time Heart Rate Anomaly Warning

During daily activities, the smart bracelet receives real-time heart rate data streams through Apache Kafka. If the heart rate consistently exceeds a preset threshold (e.g., resting heart rate > 100 bpm), the system triggers an immediate alert, warning the user of potential fatigue or cardiovascular issues.

Anomaly Detection

Introducing self-supervised learning models to identify health anomalies by analyzing latent distribution changes in user data.

Generating real-time health alerts (e.g., predicting cardiovascular events or detecting acute conditions).

Example: Acute Hypoglycemia Warning

For diabetic patients, the model analyzes real-time blood glucose data and exercise status to predict hypoglycemia risk. For instance, if blood sugar levels drop rapidly after exercise, the system prompts the user to promptly replenish glucose.

Health Trend Prediction

Using time-series modeling to predict future health trends, enabling the early development of preventive health plans.

Example: Cardiovascular Health Trend Analysis

Based on long-term heart rate variability (HRV) and blood pressure data, the model uses time-series analysis to predict future cardiovascular health trends. For example, if the model predicts an increase in cardiovascular risk within the next three months, it will proactively recommend dietary adjustments, more aerobic exercise, or medical checkups.