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Estimation of Health-Related Physical Fitness (HRPF) Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets

Author

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  • Seung-Hun Lee

    (Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Hyeon-Seong Ju

    (Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Sang-Hun Lee

    (Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Sung-Woo Kim

    (Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Hun-Young Park

    (Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
    Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Seung-Wan Kang

    (Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Young-Eun Song

    (Department of Electrical Engineering, Hoseo University, 20 Hoseo-ro 79 beon-gil, Baebang-eup, Asan-si 31499, Korea)

  • Kiwon Lim

    (Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
    Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
    Department of Physical Education, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Hoeryong Jung

    (Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
    Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

Abstract

Estimation of health-related physical fitness (HRPF) levels of individuals is indispensable for providing personalized training programs in smart fitness services. In this study, we propose an artificial neural network (ANN)-based estimation model to predict HRPF levels of the general public using simple affordable physical information. The model is designed to use seven inputs of personal physical information, including age, gender, height, weight, percent body fat, waist circumference, and body mass index (BMI), to estimate levels of muscle strength, flexibility, maximum rate of oxygen consumption (VO 2max ), and muscular endurance. HRPF data (197,719 sets) gathered from the National Fitness Award dataset are used for training (70%) and validation (30%) of the model. In-depth analysis of the model’s estimation accuracy is conducted to derive optimal estimation accuracy. This included input/output correlation, hidden layer structures, data standardization, and outlier removals. The performance of the model is evaluated by comparing the estimation accuracy with that of a multiple linear regression (MLR) model. The results demonstrate that the proposed model achieved up to 10.06% and 30.53% improvement in terms of R 2 and SEE, respectively, compared to the MLR model and provides reliable estimation of HRPF levels acceptable to smart fitness applications.

Suggested Citation

  • Seung-Hun Lee & Hyeon-Seong Ju & Sang-Hun Lee & Sung-Woo Kim & Hun-Young Park & Seung-Wan Kang & Young-Eun Song & Kiwon Lim & Hoeryong Jung, 2021. "Estimation of Health-Related Physical Fitness (HRPF) Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets," IJERPH, MDPI, vol. 18(19), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10391-:d:648865
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    References listed on IDEAS

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    3. Rabiu Muazu Musa & Anwar P. P. Abdul Majeed & Zahari Taha & Siow Wee Chang & Ahmad Fakhri Ab. Nasir & Mohamad Razali Abdullah, 2019. "A machine learning approach of predicting high potential archers by means of physical fitness indicators," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-12, January.
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