Abstract
Athletic performance seeks to enhance the physical and mental state of the athlete, and it is implicit to systematize sports training; for this reason, managing the monitoring of the physical state of athletes implies relating variables such as initial physical performance, hydration, types of nutrition, and training to take successful strategic actions. Among the innumerable factors that promote success in sport, training, motivation and, above all, resistance to bodily injury is limited since the time to test the athlete in respect to his talent and training the margin of risk between victory and defeat is minimal, nutrition is a fundamental element in the physical preparation of a specific athlete of those who are very disciplined because the diet benefits or affects sports performance and therefore the desired result. It must be taken into account that each athlete must be aware of the nutritional objectives to be achieved and how they can improve a feeding strategy to comply with the expert’s guidance and in this way shape their long, medium and short term goals. This study was a focus on planning and training of military athletes and proposes the structure of an expert nutritional system to reach better standards. The system is available through the use of web services focused on new technologies based on inferential statistical systems and fuzzy logic applying a statistic analysis aimed at decision making. The web services are aimed at improving the monitoring of the physical condition of the athletes to provide nutritional support for the maintenance of a healthy lifestyle with good physical performance, that is to say, it simplifies the information contained in the original variables, looking for the interrelations between the numerical variables defined simultaneously in a set of elements. The results achieved show an improved performance in the physical evaluation of athletes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
González Rico, R., Ramírez Lecguga, J.: Review of Physical Fitness Assessment Tests in Secondary Education. Spain (2017)
Cajamarca Cadme, E.L., Cajamarca Cuji, H.A.: Lipid Profile in Athletes Belonging to the Azuay Sports Federation, Cuenca 2017. Cuenca (2017)
Hernandez, E.F.: Relationship Between Physical Exercise and Body Temperature in Physically Active and Inactive Adults. Bogota (2014)
Ochoa Granda, E.G.: Assessment of Initial Physical Performance and Strategic Actions to Reduce Sports Dropout in Women’s Athletics of the Sports Federation of Loja. Sangolquí (2018)
Cordente Martínez, C.A., García Soidán, P., Sillero Quintana, M., Domínguez Rome-ro, J.: Relationship of the level of physical activity, blood pressure and body fat in Madrid adolescents (2007)
Quisbert Quispe, U.: Expert System Based on Fuzzy Logic for the Child-Bearing Initiation of Children in the Growth Stage. La Paz (2015)
Riza c. Berkan sheldon L.trubatch.: Fuzzy System Design Principles. IEEE Press (1997)
Hudson, d.l., Cohen, m.e.: Neural Networks and Artificial Intelligence for Biomedical Engineering. IEEE Press series on Biomedical Engineering 1999 Wiley-IEEE Press (1999)
Véliz, C.: Multivariate Analysis, Multivariate Statistical Methods for Research. Editorial Cengage Learning, Mexico (2016)
Cuadras, C.: New Methods of Multivariate Analysis. CMC Editions Editorial, Barcelona (2019)
Montanero, J.: Multivariate Analysis. Editorial University of Extremadura, Spain (2008)
Baillo, A., Grané A.: 100 Solved Problems of Multivariate Statistics, implemented in Matlab. Delta Publishing Publications, Madrid (2008)
ALP, A.: Effects of aerobic exercise on bone-specific alkaline phosphatase and urinary CTX levels in Premenopausal Women. BMC Musculoskelet Disorders, pp. 30–31 (2013)
Maimoun, L., Manetta, J., Couret, I., Dupuy, A.M., Mariano-Goulart, D., Micallef, J.P., Rossi, M.: The Intensity Level of Physical Exercise and the Bone Metabolism Response, vol 27. US National Library of Medicine National Institutes of Health (2011)
Taha, L.G.E.-D.: The Role of Expert System in Remote Sensing Applications, vol 18. Nova Science Publishers, Inc., p. 3/4 (2010)
Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J.: Building Machines That Learn and Think Like People. Cornell University (2016)
Bushra Alhijawi, Y.K.: Int. J. Adv. Intell. Paradigms. Inderscience Publisher (2018)
Mamlook, R.M.: A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution, vol. 2016 Hindawi (2016)
Gil-Sierra, D.G., Salcedo-Parra, O.J., Angulo, K.: Optimization using diffuse logic of analysis device for chemical components of natural ingredients based on the internet of things IoT. Sci. Mag. 1(37) (2020)
Bermudez, N.S.C., Tovar, S.M.C.: Analysis of the Physical Condition of the Military Cadets Academy General José María Córdova¨. Dialnet, pp. 131–139 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vallejos, D., Tapia, F., Aules, H., Torres, M., Bejarano, C. (2020). Expert Nutritional System for Military Athletes Based on Fuzzy Logic and Inferential Statistics. In: Rocha, Á., Paredes-Calderón, M., Guarda, T. (eds) Developments and Advances in Defense and Security. MICRADS 2020. Smart Innovation, Systems and Technologies, vol 181. Springer, Singapore. https://doi.org/10.1007/978-981-15-4875-8_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-4875-8_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4874-1
Online ISBN: 978-981-15-4875-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)