How to Detect Overtraining Before It Hits: Analyzing HRV with Python and Isolation Forests 🏃♂️📉
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…
Overtraining Syndrome (OTS) strikes when you can't get out of bed, resting heart rate is spiked, and motivation wanes. Heart Rate Variability (HRV) through analysis reveals Autonomic Nervous System (ANS) status. This guide explains a Python pipeline fetching Oura Cloud API data, calculating SDNN and RMSSD metrics, and utilizing Isolation Forest for anomaly detection.
The flow diagram illustrates data movement from Oura Ring to prediction. Prerequisites include Python 3.9+, Scikit-learn, SciPy/NumPy, and Oura Cloud API access. The Python code fetches HRV data, calculates SDNN and RMSSD after filtering artifacts, and detects overtraining risk using Isolation Forest. The script provides a foundation for a health-tech application but suggests improvements like Butterworth filters and personalized baseline shifting.
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