Shubhankurydv/AthleteGuard-Cortisol-Prediction

AthleteGuard — Non-Invasive Cortisol Prediction

A machine learning system that predicts cortisol levels from wearable physiological signals (HRV, ECG, EDA, EMG, Respiration) without blood tests.

Model Details

• Model: Gradient Boosting Regressor (tuned)
• Dataset: WESAD (15 subjects, chest-worn sensors)
• Target: RMSSD (cortisol proxy)
• Validation: Leave-One-Subject-Out Cross-Validation (LOSO-CV)

Performance

Metric	Value
Test MAE	6.82 ms
Test R²	0.859
LOSO Mean MAE	9.93 ± 7.31 ms
LOSO Mean R²	0.756

Outputs

1. Recovery Score (0-100) — Daily training readiness
2. Overtraining Risk Index — Low / Medium / High via ACWR
3. Pre-Competition Zone — Underactivated / Optimal / Overactivated

Additional Models

• LSTM + Attention — temporal cortisol trend tracking
• Autoencoder — physiological anomaly detection (76.4% stress detection rate)

Authors

Shubhankur — SRM Institute of Science and Technology