CIOMP OpenIR
Construction of a Machine Learning Model to Estimate Physiological Variables of Speed Skating Athletes Under Hypoxic Training Conditions
J. H. Han; M. Y. Liu; J. Z. Shi and Y. G. Li
2023
Source PublicationJournal of Strength and Conditioning Research
ISSN1064-8011
Volume37Issue:7Pages:1543-1550
AbstractHan, J, Liu, M, Shi, J, and Li, Y. Construction of a machine learning model to estimate physiological variables of speed skating athletes under hypoxic training conditions. J Strength Cond Res 37(7): 1543-1550, 2023-Monitoring changes in athletes' physiological variables is essential to create a safe and effective hypoxic training plan for speed skating athletes. This research aims to develop a machine learning estimation model to estimate physiological variables of athletes under hypoxic training conditions based on their physiological measurements collected at sea level. The research team recruited 64 professional speed skating athletes to participate in a 10-week training program, including 3 weeks of sea-level training, followed by 4 weeks of hypoxic training and then a 3-week sea-level recovery period. We measured several physiological variables that could reflect the athletes' oxygen transport capacity in the first 7 weeks, including red blood cell (RBC) count and hemoglobin (Hb) concentration. The physiological variables were measured once a week and then modeled as a mathematical model to estimate measurements' changes using the maximum likelihood method. The mathematical model was then used to construct a machine learning model. Furthermore, the original data (measured once per week) were used to construct a polynomial model using curve fitting. We calculated and compared the mean absolute error between estimated values of the 2 models and measured values. Our results show that the machine learning model estimated RBC count and Hb concentration accurately. The errors of the estimated values were within 5% of the measured values. Compared with the curve fitting polynomial model, the accuracy of the machine learning model in estimating hypoxic training's physiological variables is higher. This study successfully constructed a machine learning model that used physiological variables measured at the sea level to estimate the physiological variables during hypoxic training.
DOI10.1519/jsc.0000000000004058
URL查看原文
Indexed Bysci
Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/67507
Collection中国科学院长春光学精密机械与物理研究所
Recommended Citation
GB/T 7714
J. H. Han,M. Y. Liu,J. Z. Shi and Y. G. Li. Construction of a Machine Learning Model to Estimate Physiological Variables of Speed Skating Athletes Under Hypoxic Training Conditions[J]. Journal of Strength and Conditioning Research,2023,37(7):1543-1550.
APA J. H. Han,M. Y. Liu,&J. Z. Shi and Y. G. Li.(2023).Construction of a Machine Learning Model to Estimate Physiological Variables of Speed Skating Athletes Under Hypoxic Training Conditions.Journal of Strength and Conditioning Research,37(7),1543-1550.
MLA J. H. Han,et al."Construction of a Machine Learning Model to Estimate Physiological Variables of Speed Skating Athletes Under Hypoxic Training Conditions".Journal of Strength and Conditioning Research 37.7(2023):1543-1550.
Files in This Item: Download All
File Name/Size DocType Version Access License
Construction of a Ma(732KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[J. H. Han]'s Articles
[M. Y. Liu]'s Articles
[J. Z. Shi and Y. G. Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[J. H. Han]'s Articles
[M. Y. Liu]'s Articles
[J. Z. Shi and Y. G. Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[J. H. Han]'s Articles
[M. Y. Liu]'s Articles
[J. Z. Shi and Y. G. Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Construction of a Machine Learning Model to Estimate Physiological Variables of Speed Skating Athletes Under Hypoxic Training Conditions.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.