The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters

A Machine Learning Approach

Datos Bibliográficos

ID15501489
AutoresAndrea Di Credico (0000-0002-8388-9305, University of Chieti-Pescara), David Perpetuini (0000-0003-1903-0501, University of Chieti-Pescara), Piero Chiacchiaretta (0000-0003-1089-9809, University of Chieti-Pescara), Daniela Cardone (0000-0002-1506-1995, University of Chieti-Pescara), Chiara Filippini (0000-0003-2282-3537, University of Chieti-Pescara), Giulia Gaggi (0000-0002-8761-7390, University of Chieti-Pescara), Arcangelo Merla (0000-0003-1111-6255, University of Chieti-Pescara), Barbara Ghinassi (0000-0002-3529-2790, University of Chieti-Pescara), Angela Di Baldassarre (0000-0002-4473-4909, University of Chieti-Pescara, autor de correspondencia), Pascal Izzicupo (0000-0001-6944-8995, University of Chieti-Pescara)
Año2021
Volumen18
Número20
Páginas10854-10854
Fecha de publicación2021-10-15
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores de la revistaISSN: 1661-7827 • E-ISSN: 1660-4601
EditorialMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph182010854
PMID34682594
OpenAlexW3206287259
IdiomaEN
Citas recibidas1
Referencias citadas55

Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30-15 IFT is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30-15 IFT using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects

Accelerometer · Heart rate · Machine learning · Multivariate statistics · Simulation · Statistics · Support vector machine · Test (biology · Univariate · Cardiovascular and exercise physiology · Computer Science · Mathematics · Medicine · Muscle activation and electromyography studies · Sports Performance and Training · Artificial Intelligence