Are you pushing your body to the limit or just driving it into the ground? In the world of high-performance athletics and biohacking, Heart Rate Variability (HRV) has become the "North Star" for recovery. But raw numbers from your Garmin or Oura Ring only tell half the story. To truly understand the relationship between sleep quality, exercise load, and stress perception, we need more than a dashboard—we need a predictive pipeline.
In this tutorial, we will build a multi-dimensional analysis system using Scikit-learn, LSTM (Keras), and the Terra API to predict overtraining risks. By the end of this guide, you'll know how to turn messy wearable data into actionable health insights.
The Architecture: From Bio-Signals to Insights
To handle the complexity of time-series data (HRV) and categorical features (activity types), we use a hybrid approach. We use Random Forest to identify which lifestyle factors impact recovery the most and LSTM to predict future HRV trends based on historical sequences.
graph TD
