Di Credico, A.; Perpetuini, D.; Chiacchiaretta, P.; Cardone, D.; Filippini, C.; Gaggi, G.; Merla, A.; Ghinassi, B.; Di Baldassarre, A.; Izzicupo, P.
The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach. Int. J. Environ. Res. Public Health 2021, 18, 10854.
https://doi.org/10.3390/ijerph182010854
AMA Style
Di Credico A, Perpetuini D, Chiacchiaretta P, Cardone D, Filippini C, Gaggi G, Merla A, Ghinassi B, Di Baldassarre A, Izzicupo P.
The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach. International Journal of Environmental Research and Public Health. 2021; 18(20):10854.
https://doi.org/10.3390/ijerph182010854
Chicago/Turabian Style
Di Credico, Andrea, David Perpetuini, Piero Chiacchiaretta, Daniela Cardone, Chiara Filippini, Giulia Gaggi, Arcangelo Merla, Barbara Ghinassi, Angela Di Baldassarre, and Pascal Izzicupo.
2021. "The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach" International Journal of Environmental Research and Public Health 18, no. 20: 10854.
https://doi.org/10.3390/ijerph182010854
APA Style
Di Credico, A., Perpetuini, D., Chiacchiaretta, P., Cardone, D., Filippini, C., Gaggi, G., Merla, A., Ghinassi, B., Di Baldassarre, A., & Izzicupo, P.
(2021). The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach. International Journal of Environmental Research and Public Health, 18(20), 10854.
https://doi.org/10.3390/ijerph182010854
