Radial basis function (RBF) neural networks achieved the highest predictive accuracy for race-walking outcomes (R2 = 0.89; RMSE = 0.28), outperforming MLP and regression baselines.
Observational (n=30)
Do artificial neural networks improve the prediction of competitive performance in elite female race walkers compared to classical regression models?
Radial basis function neural networks offer superior predictive accuracy for race-walking outcomes compared to classical regression models.
Effect estimate: R2 0.89
This study identified key physiological, biomechanical, and strength-related predictors of competitive performance in elite female race walkers and evaluated the effectiveness of classical and machine-learning models for individualized training optimization. Thirty nationally ranked female race walkers (25 ± 3 years) were assessed over four seasons (2021–2024). Laboratory and field tests included ergospirometry (VO 2 max, VCO2, VE (minute ventilation), respiratory exchange ratio (RER)), blood lactate (LA), heart rate (HR), gait kinematics (step length, speed), and lower-limb strength (1RM, maximal power). Temporal and seasonal dynamics were evaluated using Kruskal–Wallis and g-Fisher tests. Predictive models included multiple regression, polynomial regression, multilayer perceptron (MLP), and radial basis function (RBF) networks, developed with correlation-vector analysis (R0, R1), collinearity diagnostics, and interaction terms. The most influential predictors were HR, step length, VO 2 max, and 1RM (R0 > 0.70). RBF achieved the best predictive accuracy ( R 2 = 0.89; RMSE = 0.28), outperforming MLP ( R 2 = 0.87) and regression baselines ( R 2 = 0.61–0.81). Significant seasonal variation ( p < 0.001) underscored the value of time-dependent modeling. Conclusion: RBF neural networks offer superior performance for predicting race-walking outcomes; HR and step length are key real-time indicators, whereas VO 2 max and 1RM inform longer-term adaptation.
Skalski et al. (Fri,) conducted a observational in Race walking (n=30). Radial basis function (RBF) neural networks vs. Multilayer perceptron and regression models was evaluated on Predictive accuracy for competitive performance (R2 0.89). Radial basis function (RBF) neural networks achieved the highest predictive accuracy for race-walking outcomes (R2 = 0.89; RMSE = 0.28), outperforming MLP and regression baselines.