Ever woke up feeling like a truck hit you, only to realize your Apple Watch had been screaming "Warning!" via your data for the last 24 hours?
Heart Rate Variability (HRV) is the "canary in the coal mine" for our bodies. It's a powerful metric that tracks the variation in time between each heartbeat, serving as a direct window into your Autonomic Nervous System. In this guide, we are going to build a real-time HRV anomaly detector using wearable data analysis, Scikit-learn, and AWS Lambda. By applying machine learning to time-series health data, we can identify physiological stress, potential infections, or overtraining before physical symptoms even manifest.
If you’ve been looking to dive into anomaly detection in time-series or want to master health data engineering, you’re in the right place!
The Architecture: From Heartbeat to Alert 🛠️
To achieve real-time monitoring, we need a pipeline that moves data from your wrist to a cloud-based inference engine. Here is the high-level flow:
