Python and Isolation Forest Can Help Athletes Detect Overtraining via HRV Data

A developer tutorial published on DEV Community outlines how to build a Python pipeline that detects early signs of overtraining syndrome using Heart Rate Variability (HRV) data. The system fetches raw R-R interval data from the Oura Ring via its Cloud API, then calculates two key HRV metrics — SDNN and RMSSD — to assess recovery status. An unsupervised machine learning model called Isolation Forest is applied to flag anomalous recovery patterns that may indicate an athlete is pushing too hard. Unlike fixed thresholds, Isolation Forest adapts to individual baselines, making it suitable for the high variability seen across different athletes. The guide targets developers and biohackers looking to convert raw wearable data into actionable health insights using tools like scikit-learn, NumPy, and SciPy.