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

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 s...

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Bibliographic Details
Published in:International journal of environmental research and public health 2021-10, Vol.18 (19), p.10391
Main Authors: Lee, Seung-Hun, Ju, Hyeon-Seong, Lee, Sang-Hun, Kim, Sung-Woo, Park, Hun-Young, Kang, Seung-Wan, Song, Young-Eun, Lim, Kiwon, Jung, Hoeryong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Body fat
Body mass
Body mass index
Body size
Datasets
Flexibility
Machine learning
Model accuracy
Muscle strength
Neural networks
Outliers (statistics)
Oxygen consumption
Physical fitness
Regression analysis
Standardization
Support vector machines
Training
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Summary: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 (VO2max), 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 R2 and SEE, respectively, compared to the MLR model and provides reliable estimation of HRPF levels acceptable to smart fitness applications.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph181910391