Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability
Estrella, Tony (Universitat Autònoma de Barcelona. Departament de Psicologia Bàsica, Evolutiva i de l'Educació)
Capdevila Ortís, Lluís (Universitat Autònoma de Barcelona. Departament de Psicologia Bàsica, Evolutiva i de l'Educació)

Data:	2025
Resum:	Heart rate variability (HRV) is a non-invasive health and fitness indicator, and machine learning (ML) has emerged as a powerful tool for analysing large HRV datasets. This study aims to identify athletic characteristics using the HRV test and ML algorithms. Two models were developed: Model 1 (M1) classified athletes and non-athletes using 856 observations from high-performance athletes and 494 from non-athletes. Model 2 (M2) identified an individual soccer player within a team based on 105 observations from the player and 514 from other team members. Three ML algorithms were applied -Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)- and SHAP values were used to interpret the results. In M1, the SVM algorithm achieved the highest performance (accuracy = 0. 84, ROC AUC = 0. 91), while in M2 Random Forest performed best (accuracy = 0. 92, ROC AUC = 0. 94). Based on these results, we propose an athleticism index and a soccer identification index derived from HRV data. The findings suggest that ML algorithms, such as SVM and RF, can effectively generate indices based on HRV for identifying individuals with athletic characteristics or distinguishing athletes with specific sports profiles. These insights underscore the importance of integrating HRV assessments systematically into training regimens for enhanced athletic evaluation.
Ajuts:	Agencia Estatal de Investigación PID2019-107473RB-C21 Agencia Estatal de Investigación PPID2019-107473RB-C22 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00806
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Heart rate variability ; Machine learning ; Athletes ; Sport profiles ; Team sports ; Random forest ; Support vector machine ; SHAP values ; Training load
Publicat a:	Sports, Vol. 13, Num. 2 (January 2025) , ISSN 2075-4663

DOI: 10.3390/sports13020030
PMID: 39997961

16 p, 1.5 MB

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