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Machine Learning-Based Classification of Alertness Levels in Elite Shooting Athletes Using Heart Rate Variability
Jiaojiao Lu1,2, Jun Qiu2, Yan An2
1School of Exercise and Health, Shanghai University of Sport, Shanghai, China.
Journal of Sports Science & Medicine
|June 19, 2026
Summary
This study developed a predictive model using heart rate variability (HRV) to assess alertness in shooting athletes. The AdaBoost model effectively identified optimal versus sub-optimal alertness levels, aiding in readiness assessment.
Area of Science:
- Sports Science
- Psychophysiology
- Machine Learning in Sports
Background:
- Elite athletes require sustained attention during competition.
- Monitoring alertness is crucial for performance and safety.
- Heart Rate Variability (HRV) offers a non-invasive window into physiological stress.
Purpose of the Study:
- To develop a predictive model for alertness in elite shooting athletes.
- To analyze Heart Rate Variability (HRV) dynamics under simulated competitive stress.
- To identify key HRV predictors of alertness using machine learning.
Main Methods:
- 83 national-level shooting athletes underwent a 60-minute Psychomotor Vigilance Task (PVT).
- Continuous HRV data were recorded and analyzed for key features.
- Machine learning algorithms (SVM, RF, XGBoost, AdaBoost) built binary classification models for alertness.
Main Results:
- The AdaBoost model achieved the highest performance (Accuracy: 0.75, F1-score: 0.73, AUC: 0.77).
- Very Low Frequency percentage (VLF%) was the most significant predictor of alertness.
- Elevated VLF% correlated with decreased alertness levels.
Conclusions:
- A binary classification model integrating HRV indices (VLF%) and AdaBoost effectively distinguishes alertness levels in shooting athletes.
- This provides a validated, non-invasive tool for objective psychophysiological monitoring.
- The model offers actionable insights for pre-competition readiness assessment in sports training.
