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Test method for health-related physical fitness of college students in mobile internet environment

1 School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2 Department of Ultrasound, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
3 Department of Sports, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Special Issues: Machine Learning and Big Data in Medical Image Analysis

Improving health-related physical fitness plays an important role in enhancing the comprehensive physical fitness of college students. The traditional test of health-related physical fitness is characterized by complicated operations and low efficiency. A set of test methods for health-related physical fitness of college students based on an intelligent mobile terminal is designed in this work. The intelligent test method calculates the health-related physical fitness level of users through image acquisition and analysis by combining intelligent terminal software and test items of key elements. To verify the validity of the proposed method, a total of 116 college students (59 males and 57 females) aged between 18 and 22 are chosen as test respondents. The health-related physical fitness of respondents is tested and compared by using the traditional and proposed methods. The traditional test method reports the cardiorespiratory fitness scores of 69.6 ± 9.5 (M) and 77.1 ± 9.9 (F), and the proposed method reports 70.9 ± 9.7 (M) and 77.7 ± 9.8 (F). Twenty samples are chosen randomly to calculate the correlation coefficients r = 0.944 (M) and 0.965 (F) and significance p = 0.145 (M) and 0.489 (F). The flexibility fitness scores in the traditional method are 74.6 ± 11.9 (M) and 73.3 ± 11.5 (F), and the scores of the proposed method are 74.8 ± 11.5 (M) and 75.0 ± 11.2 (F). Twenty samples are chosen randomly to calculate r = 0.944 (M) and 0.938 (F) and p = 0.941 (M) and 0.098 (F). In the proposed method, muscle strength/muscular endurance fitness and a body composition module are tested by the traditional method, and test data are input into an artificial input system. The experimental results verify the accuracy of the proposed method in evaluating the health-related physical fitness of college students. Application of the proposed method can effectively reduce the cost of physical fitness testing, increase the convenience of testing, and direct substantial attention to the health-related physical fitness of college students.
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