Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network

Ahmad, Zair Asrar

doi:10.19080/jpfmts.2018.03.555624

Cited by 2 publications

(1 citation statement)

References 16 publications

(17 reference statements)

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“…Hulme et al (2019) also encouraged data driven computational modelling applications. The utilisation of personal data from running wearables to drive the simulation model adds to the personalisation and preciseness of the model, similar to how Kosmidis and Passfield (2015) and Ahmad, Jamaludin, and Hafidz Omar (2018) found subject-specific metrics in their respective analyses of data from running wearables.…”

Section: Results In Contextmentioning

confidence: 89%

Computational modelling in sport: a hybrid simulation of the runner as a complex adaptive system

Vermeulen

Grobbelaar

Botha

et al. 2021

Theoretical Issues in Ergonomics Science

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Running-related injuries, specifically overuse injuries, are complex and multifaceted. A different way of thinking is required to fully comprehend why, and how, these injuries occur. The systems thinking perspective offers tools and techniques to capture system-wide interactions in causal, closed-loop structures which may be studied and quantified in a practical way. The value of systems thinking in sport may only realise when pragmatic models follow descriptive, qualitative models. This article instantiates a qualitative, systems thinking perspective of the runner as a complex adaptive system with a hybrid simulation model. The approach is substantiated with principles from physics and physiology and is partially driven by data from a running watch. Results demonstrate that the scientific, reductionist method may be augmented with dynamic, closed-loop thinking and simulation modelling.

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Section: Results In Contextmentioning

confidence: 89%

Computational modelling in sport: a hybrid simulation of the runner as a complex adaptive system

Vermeulen

Grobbelaar

Botha

et al. 2021

Theoretical Issues in Ergonomics Science

View full text Add to dashboard Cite

show abstract

From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes

Tomaszewski,

Lukanova-Jakubowska,

Majorczyk

et al. 2024

PLoS ONE

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Lactate analysis plays an important role in sports science and training decisions for optimising performance, endurance, and overall success in sports. Two parameters are widely used for these goals: aerobic (AeT) and anaerobic (AnT) thresholds. However, determining AeT proves more challenging than AnT threshold due to both physiological intricacies and practical considerations. Thus, the aim of this study was to determine AeT and AnT thresholds using machine learning modelling (ML) and to compare ML-obtained results with the parameters’ values determined using conventional methods. ML seems to be highly useful due to its ability to handle complex, personalised data, identify nonlinear relationships, and provide accurate predictions. The 183 results of CardioPulmonary Exercise Test (CPET) accompanied by lactate and heart ratio analyses from amateur athletes were enrolled to the study and ML models using the following algorithms: Random Forest, XGBoost (Extreme Gradient Boosting), and LightGBM (Light Gradient Boosting Machine) and metrics: R2, mean absolute error (MAE), mean squared error (MSE) and root mean square error (RMSE). The regressors used belong to the group of ensemble learning algorithms that combine the predictions of multiple base models to improve overall performance and counteract overfitting to training data. Based on evaluation metrics, the following models give the best predictions: for AeT: Random Forest has an R2 value of 0.645, MAE of 4.630, MSE of 44.450, RMSE of 6.667; and for AnT: LightGBM has an R2 of 0.803, the highest among the models, MAE of 3.439, the lowest among the models, MSE of 20.953, and RMSE of 4.577. Outlined research experiments, a comprehensive review of existing literature in the field, and obtained results suggest that ML models can be trained to make personalised predictions based on an individual athlete’s unique physiological response to exercise. Athletes exhibit significant variation in their AeT and AT, and ML can capture these individual differences, allowing for tailored training recommendations and performance optimization.

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Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network

Cited by 2 publications

References 16 publications

Computational modelling in sport: a hybrid simulation of the runner as a complex adaptive system

Computational modelling in sport: a hybrid simulation of the runner as a complex adaptive system

From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes

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