Towards Simplifying Assessment of Athletes Physical Fitness: Evaluation of the Total Physical Performance by Means of Machine Learning

Kublanov, Vladimir S.; Dolganov, Anton Yu.; Badtieva, V A; Akopyan, David

doi:10.5220/0007699105390544

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“…and obesity in adolescents and performed a cluster analysis. Some studies [6,7] have worked on machine learning to predict the classi cation of physical tness levels rather than exploring the intrinsic relationships between tness metrics. Yin et al [8] analyzed height, weight, vital capacity, step test, grip strength, and vertical jump through decision trees and found that the most inuential indicator for boys was vital capacity, while for girls it was the step test.…”

Section: Introductionmentioning

confidence: 99%

Revealing the Inner-relevance of College Students’ Physical Fitness by Association Analysis and Neural Network

Pang

Wang

2022

Computational Intelligence and Neuroscience

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Background: The physical activity and health status of the students in China are not optimistic, there is a general lack of exercise volume and exercise intensity. Normal college students shoulder the future of China’s education. Promoting their physical health is the basic requirement for cultivating teachers in the new era;Methods:Testing and recording 1123 male, 3266 female college students’ physical fitness indicators in a normal college, the relationship between these indicators was mined by correlation analysis and Apriori, and the intelligent prediction models was constructed according to the mined knowledge;Results: There was no correlation between male 1000m running and vital capacity (P > 0.05), but it was correlated with vital capacity weight index (P < 0.05); Most indicators of women showed varying degrees of correlation. There are many association rules between female 50m sprint and standing long jump, sit-ups, and BMI. The introduction of vital capacity weight index slightly improved the accuracy of the 1000m run prediction model; The prediction model of female 50m sprint with standing long jump, sit-ups and BMI as inputs not only keeps the accuracy in a reasonable range, but also reduces the complexity and parameters;ConclusionsFor male students, the ostensibly paradoxical relationships between vital capacity and a 1000 meter run and between vital capacity and pull up were actually due to body shape; Body shape, lower limb explosive power, and core strength play key roles for female college students’ speed quality; BMI, standing long jump and one minute sit-up can be used to predict the 50m sprint performance of general female college students.

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Section: Introductionmentioning

confidence: 99%