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Related Experiment Video

Updated: May 28, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

Artificial neural network predictive models for optimizing the training process in race walking: a longitudinal

Dariusz Skalski1, Magdalena Prończuk1, Kinga Łosińska1

  • 1Gdańsk University of Physical Education and Sport, Gdansk, Poland.

Peerj
|May 27, 2026
PubMed
Summary

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Elite female race walkers

Area of Science:

  • Sports Science
  • Biomechanics
  • Exercise Physiology

Background:

  • Optimizing training for elite athletes requires understanding performance predictors.
  • Individualized training plans enhance competitive success in race walking.
  • Physiological and biomechanical factors significantly influence race walking performance.

Purpose of the Study:

  • To identify key physiological, biomechanical, and strength predictors of competitive performance in elite female race walkers.
  • To evaluate classical and machine learning models for optimizing individualized training programs.
  • To analyze temporal and seasonal dynamics influencing race walking performance.

Main Methods:

  • Assessed 30 elite female race walkers over four seasons (2021-2024).
Keywords:
Machine learningPhysiological monitoringRace walking performanceTraining adaptation

Related Experiment Videos

Last Updated: May 28, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

  • Conducted laboratory and field tests measuring VO2max, blood lactate, heart rate, gait kinematics (step length, speed), and lower-limb strength (1RM, maximal power).
  • Employed Kruskal-Wallis and g-Fisher tests for temporal/seasonal analysis; developed predictive models including multiple regression, MLP, and RBF networks.

    • Main Results:

    • Heart rate (HR), step length, maximal oxygen uptake (VO2max), and one-repetition maximum (1RM) were the most influential predictors (R0 > 0.70).
    • Radial Basis Function (RBF) networks demonstrated superior predictive accuracy (R² = 0.89; RMSE = 0.28), outperforming Multilayer Perceptron (MLP) and regression models.
    • Significant seasonal variations (p < 0.001) highlight the importance of time-dependent modeling.

    Conclusions:

    • Radial Basis Function neural networks provide superior predictive performance for race walking outcomes.
    • Heart rate and step length serve as crucial real-time performance indicators.
    • VO2max and 1RM are vital for informing long-term training adaptations in elite race walkers.