We’ve all been there: you're crushing your workouts, feeling like a beast, and then suddenly—bam. You can’t get out of bed, your resting heart rate is through the roof, and your motivation has evaporated. Welcome to Overtraining Syndrome (OTS).
In the world of sports science, Heart Rate Variability (HRV) is the gold standard for tracking recovery. By analyzing the tiny fluctuations between heartbeats (R-R intervals), we can peek into our Autonomic Nervous System (ANS). Today, we’re going to build a Python-based pipeline to fetch data from the Oura Cloud API, calculate key HRV metrics like SDNN and RMSSD, and use an Isolation Forest model to detect when you're pushing a bit too hard.
Whether you're a biohacker or a developer interested in wearable data analysis, this guide will show you how to turn raw health data into actionable recovery insights.
The Architecture: From Pulse to Prediction 🏗️
Before we dive into the code, let's visualize how the data flows from your finger to our anomaly detection model.
