Forecasting Tomorrow's Recovery: A Deep Learning Approach to Oura Readiness Prediction

Advanced predictive modeling for Oura Ring readiness scores using deep learning with attention mechanisms.

🎯 Project Overview

The Innovation: Most Oura analyses are descriptive ("What was my readiness yesterday?")
This project is PREDICTIVE ("What will my readiness be tomorrow?")

Key Features

1. Multi-day Forecasting: Predict readiness 1-7 days ahead
2. Attention Mechanisms: Identify which past days matter most for prediction
3. Uncertainty Quantification: Provide confidence intervals for predictions
4. Personalized Calibration: Learns individual user patterns
5. Actionable Recommendations: Suggest what to do today for better tomorrow

🔥 What Makes This Outstanding

• ✅ Uses Oura's Actual API Structure (studied from GitHub repos)
• ✅ Synthetic but Realistic Data (no Oura Ring needed)
• ✅ Advanced ML (LSTM + Attention mechanisms)
• ✅ Production Quality (proper validation, documentation)
• ✅ Novel Contribution (predictive, not descriptive)

📊 Dataset

• Synthetic Oura Data: Generated based on real Oura API v2 structure
• Features: Sleep scores, activity scores, HRV, temperature, readiness contributors
• Temporal: Multi-day sequences for time-series prediction
• Personalization: User-specific baselines and patterns

🗂️ Project Structure

oura_readiness_prediction/
├── src/
│   ├── data_generation.py      # Synthetic Oura data generator
│   ├── feature_engineering.py   # Create forecast features
│   ├── train_baseline.py       # Random Forest, XGBoost
│   ├── train_advanced.py       # LSTM with Attention
│   ├── train_final_lstm.py     # Optimized LSTM model
│   ├── train_ensemble.py       # Ensemble model (RF + LSTM)
│   ├── tune_lstm.py            # Hyperparameter tuning
│   └── create_visualizations.py # Model comparison plots
├── data/
│   ├── raw/                    # Generated synthetic data
│   └── processed/             # Processed features
├── models/                     # Trained models
├── outputs/                    # Results & visualizations
├── notebooks/                  # EDA notebooks
└── README.md

🚀 Quick Start

Prerequisites

pip install -r requirements.txt

Run Pipeline

# Step 1: Generate synthetic Oura data
python src/data_generation.py

# Step 2: Create forecast features
python src/feature_engineering.py

# Step 3: Train baseline models
python src/train_baseline.py

# Step 4: Train advanced LSTM model
python src/train_advanced.py

# Step 4: Train optimized LSTM model
python src/train_final_lstm.py

# Step 5: Train ensemble model
python src/train_ensemble.py

# Step 6: Create visualizations
python src/create_visualizations.py

🔬 Methodology

Baseline Models

• Random Forest Regressor: Traditional ML approach
• XGBoost Regressor: Gradient boosting

Advanced Model

• LSTM Optimized: Deep learning for sequential data
  • 1-layer LSTM architecture (optimized)
  • 64 hidden units
  • 7-day sequence lookback
  • 36 selected time-series features
  • R² = 0.592 (improved from 0.550 through tuning)

Ensemble Model

• RF + LSTM Ensemble: Combines strengths of both models
  • Best configuration: RF 90% + LSTM 10%
  • R² = 0.832, MAE = 2.18 points
  • Provides regularization and robustness

Features

• Lag features (previous 1, 2, 3, 7 days)
• Rolling averages (3-day, 7-day)
• Training strain (acute:chronic load ratio)
• Sleep debt tracking
• HRV and temperature deviations

📈 Results

Model Performance

Model	R²	MAE (points)	RMSE (points)	Status
Random Forest	0.869	1.89	2.44	✅ Best Model
Ensemble (RF 90% + LSTM 10%)	0.832	2.18	2.77	✅ Excellent
XGBoost	0.699	3.06	3.82	✅ Strong Baseline
LSTM Optimized	0.592	3.63	4.44	✅ Improved from 0.550

Key Achievements

✅ Random Forest achieves R² = 0.869 - Excellent predictive performance!
✅ 73 engineered features created from Oura data structure
✅ 4,500 synthetic records (50 users × 90 days)
✅ Ensemble model combining RF + LSTM
✅ Hyperparameter tuning improved LSTM by 7.6%
✅ Production-ready code with proper validation

Dataset Statistics

• Total Records: 4,500 (after feature engineering: 4,100)
• Users: 50
• Time Period: 90 days per user
• Features: 73 engineered features
• Target: Tomorrow's readiness score (0-100)

💡 Relevance to Oura Ring

This project demonstrates:

✅ Predictive Health Monitoring (forecast readiness, not just report it)
✅ Deep Learning Expertise (LSTM, attention mechanisms)
✅ Time Series Forecasting (multi-day ahead predictions)
✅ Personalization (user-specific models)
✅ Production-Ready ML (proper validation, uncertainty quantification)
✅ Understanding of Oura Data (API structure, physiological relationships)

🛠️ Technologies

• Python 3.10+
• pandas, numpy - Data processing
• scikit-learn - Baseline ML models
• XGBoost - Gradient boosting
• PyTorch/TensorFlow - Deep learning (LSTM + Attention)
• SHAP - Explainability
• matplotlib, seaborn - Visualization

👤 Author

Md Karim Uddin, PhD
PhD Veterinary Medicine | MEng Big Data Analytics
Postdoctoral Researcher, University of Helsinki

• GitHub: @mdkarimuddin
• LinkedIn: Md Karim Uddin, PhD

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built to demonstrate advanced ML capabilities for wearable health technology roles.