Developer Builds AI Overtraining Detector Using HRV Data and Isolation Forest

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A developer tutorial on DEV Community outlines how to build a personalized overtraining detection system using Heart Rate Variability data from wearables like the Oura Ring or Apple Watch. The project uses Python, Scikit-learn's Isolation Forest algorithm, and the Oura Cloud API to flag days when recovery metrics fall significantly below a user's baseline. Isolation Forest was chosen because it detects anomalies without assuming a normal data distribution, making it well-suited for irregular biometric time-series data. The model is configured to identify roughly the bottom 5% of HRV readings as potential overtraining days, then visualizes those warning periods using Matplotlib. The tutorial notes that scaling such a system for production use would require additional work around data drift, API rate limiting, and feature engineering.

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