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Article

The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis

1
Embedded Systems and Artificial Intelligence Group, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain
2
Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain
3
Triathlon Technification Program, Valencian Community Triathlon Federation, 46940 Manises, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe García-Pinillos
Sensors 2021, 21(7), 2422; https://doi.org/10.3390/s21072422
Received: 4 March 2021 / Revised: 29 March 2021 / Accepted: 30 March 2021 / Published: 1 April 2021
(This article belongs to the Special Issue Wearable Sensors & Gait)
Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO2max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO2max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power. View Full-Text
Keywords: VO2max; power; running biomechanics; hierarchical cluster analysis; machine learning; triathletes VO2max; power; running biomechanics; hierarchical cluster analysis; machine learning; triathletes
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MDPI and ACS Style

Pardo Albiach, J.; Mir-Jimenez, M.; Hueso Moreno, V.; Nácher Moltó, I.; Martínez-Gramage, J. The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis. Sensors 2021, 21, 2422. https://doi.org/10.3390/s21072422

AMA Style

Pardo Albiach J, Mir-Jimenez M, Hueso Moreno V, Nácher Moltó I, Martínez-Gramage J. The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis. Sensors. 2021; 21(7):2422. https://doi.org/10.3390/s21072422

Chicago/Turabian Style

Pardo Albiach, Juan, Melanie Mir-Jimenez, Vanessa Hueso Moreno, Iván Nácher Moltó, and Javier Martínez-Gramage. 2021. "The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis" Sensors 21, no. 7: 2422. https://doi.org/10.3390/s21072422

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