Tutorial: Predicting Overtraining Risk Using HRV Data, Random Forest, and LSTM

A technical tutorial published on DEV Community outlines how to build a stress and recovery prediction system using wearable device data. The guide uses the Terra API to standardize health metrics from devices like Garmin watches and Oura rings into a unified data format. A hybrid machine learning approach combines Random Forest to identify which lifestyle factors most influence recovery and an LSTM neural network to forecast future HRV trends from historical sequences. The full pipeline is designed to expose predictions via a FastAPI endpoint, feeding an end-user health dashboard. The project requires Python 3.9+, Scikit-learn, Keras/TensorFlow, and Terra API credentials.