스포츠 의과학에서 인공 신경망 분석 알고리즘의 가능성
Feasibility of Artificial Neural Network Analysis Algorithm in Sports Medicine and Science
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
오늘날 스포츠의과학 분야에서는 건강관리 서비스의 개념으로 플랫폼에 적용할 수 있는 다양한 알고리즘이 개발되고 있다. 그러나 아직까지 분석에 있어 머신러닝 기반 인공신경망 분석이 아닌 다중회귀모델을 주로 활용하고 있는 실정이다. 머신러닝 기반 인공신경망 분석은 비선형 활성화 함수 이용하여 추정하고자 하는 변인의 직선 특성 뿐만 아니라 비선형 특성도 표현할 수 있는 장점을 가지며, 이에 효과적인 추정을 위한 강 력한 도구로 사용될 수 있으며, 선행연구를 통해 더 높은 추정 정확도를 나타내고 있다. 이에 스포츠의과학 분야에서 다양한 체력관련 요인들의 측정이 많은 비용, 시간, 고가의 장비, 대상자의 노력 등이 소요됨을 고 려했을 때, 다중회귀모델 보다 검정력에 있어 보다 효과적인 인공신경망 분석을 통해 편리하고 효용성 있는 고성능의 알고리즘을 개발하는 것은 학문적 발전과 현장에의 적용에 있어서 매우 중요한 과업이며, 이는 스 포츠의과학 분야의 산업적 기여도를 높일 수 있는 것으로 판단된다.
Abstract
Today, various algorithms for estimating health-related fitness that can be applied to a smart fitness platform are being developed. However, in the field of sports medicine science, multiple regression models are mainly used to estimate health-related fitness, not machine learning-based artificial neural network analysis. Machine learning-based artificial neural network analysis has the advantage of expressing nonlinear characteristics as well as linear characteristics of variables to be estimated using nonlinear activation functions. Therefore, it can be used as a powerful tool for effective estimation, and shows higher estimation accuracy through several previous studies. Measurement of various physical fitness-related factors in the field of sports medicine requires a lot of cost, time, expensive equipment, and effort of the subject. Therefore, developing a convenient and effective high-performance algorithm through artificial neural network analysis that is more effective in estimated power than the multiple regression model is a very important task for academic development and field application, which will increase the industrial contribution of sports medicine science.
참고문헌 (26)
- Akay, M. F., "Artificial neural network-based model for predicting VO2max from a submaximal exercise test", Expert Systems with Applications, 38(3), pp.2007~2010 (2011)
- Alahmari, K. A., "Hand grip strength determination for healthy males in Saudi Arabia : A study of the relationship with age, body mass index, hand length and forearm circumference using a hand-held dynamometer", Journal of International Medical Research, 45(2), pp.540~548 (2017)
- Andersen, L. B., "An intermittent running test to estimate maximal oxygen uptake : the Andersen test", Journal of Sports Medicine and Physical Fitness, 48(4), pp.434~437 (2008)
- Badawi, A., "Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection", Journal of Public Health, 10(1), pp.1985~ (2021)
- Beltrame, T., "Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs", Journal of Applied Physiology, 121(5), pp.1226~1233 (2016)
- Cao, Z. B., "Prediction of VO2max with daily step counts for Japanese adult women", European Journal of Applied Physiology, 105(2), pp.289~296 (2009)
- Cho, H. L., "Development of multistage 10-m shuttle run test for VO2max estimation in healthy adults", Journal of Men’s Health, 2021, pp.1~8 (2021)
- 함주호, "Development of an anaerobic threshold (HRLT, HRVT) estimation equation using the heart rate threshold (HRT) during the treadmill incremental exercise test", Physical Activity and Nutrition, 21(3), pp.43~49 (2017), 한국운동영양학회[DOI]
- Hwang, J., "A deep learning-based method for grip strength prediction:Comparison of multilayer perceptron and polynomial regression approaches", Plos one, 16(2), pp.e0246870~ (2021)
- Kim, S. W., "Estimation of health-related physical fitness using multiple linear regression in korean adults : national fitness award(NFA)2015-2019", Frontiers in Physiology, 12, pp.659~ (2021)
- Kim, S. W., "Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study", INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 58, pp.1~10 (2021)
- La Delfa, N. J., "The ‘Arm Force Field’ method to predict manual arm strength based on only hand location and force direction", Applied Ergonomics, 59, pp.410~421 (2017)
- Lee, S. H., "Estimation of Health-Related Physical Fitness (HRPF)Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets", International Journal of Environmental Research and Public Health, 18(19), pp.10391~ (2021)
- Leibetseder, V. J., "A simple running test to estimate cardiorespiratory fitness", Journal of Exercise Physiology, 5(3), pp.6~13 (2002)
- Malek, , M. H., "A new nonexercise-based VO2max prediction equation for aerobically trained men", The Journal of Strength & Conditioning Research, 19(3), pp.559~565 (2005)
- Mikawa, K., "Development of a field test for evaluating aerobic fitness in middle-aged adults : Validity of a 15-m incremental shuttle walk and run test", Journal of Sports Science & Medicine, 10(4), pp.712~717 (2011)
- Noorbakhsh-Sabet, N., "Artificial intelligence transforms the future of health care", The American Journal of Medicine, 132(7), pp.795~801 (2019)
- Park, H. Y., "Estimated Artificial Neural Network Modeling of Maximal Oxygen Uptake Based on Multistage 10-m Shuttle Run Test in Healthy Adults", International Journal of Environmental Research and Public Health, 18(16), pp.8510~ (2021)
- Przednowek, K., "Predictive modeling of VO2max based on 20m shuttle run test for young healthy people", Applied Sciences, 8(11), pp.2213~ (2018)
- Sakr, S., "Comparison of machine learning techniques to predict all-cause mortality using fitness data : the Henry ford exercIse testing(FIT)project", BMC Medical Informatics and Decision Making, 17(1), pp.1~15 (2017)
- Sakr, S., "Using machine learning on cardiorespiratory fitness data for predicting hypertension:The Henry Ford ExercIse Testing (FIT) Project", PLoS One, 13(4), pp.e0195344~ (2018)
- Sanada, K., "Development of non exercise prediction models of maximal oxygen uptake in healthy Japanese young men", European Journal of Applied Physiology, 99(2), pp.143~148 (2007)
- Stickland, M. K., "Prediction of maximal aerobic power from the 20-m multi-stage shuttle run test", Canadian Journal of Applied Physiology, 28(2), pp.272~282 (2003)
- Sui, X., "Cardiorespiratory fitness and adiposity as mortality predictors in older adults", Journal of the American Medical Association, 298(21), pp.2507~2516 (2007)
- Thompson, W. R., "Worldwide survey of fitness trends for 2022", ACSM's Health &Fitness Journal, 26(1), pp.11~20 (2022)
- Zhang, Y., "Applying artificial intelligence methods for the estimation of disease incidence : The Utility of Language Models", Frontiers in Digital Health, 2, pp.569261~ (2020)