Achieve Personalized Exercise Intensity through an Intelligent System and Cycling Equipment: A Machine Learning Approach

doi:10.3390/app10217688

HFCAS OpenIR

	Achieve Personalized Exercise Intensity through an Intelligent System and Cycling Equipment: A Machine Learning Approach
	Wu, Yichen 1,2,3; Ma, Zuchang1 ; Zhao, Huanhuan 1,2,4; Li, Yibing 1,2,5; Sun, Yining1
	2020-11-01
发表期刊	APPLIED SCIENCES-BASEL
通讯作者	Sun, Yining(health-promotion@iim.ac.cn)
摘要	Featured Application With the development of artificial intelligence, Internet technology, smart exercise equipment, and smart wearable devices, more and more people are becoming willing to use these technologies for exercise, whether out of curiosity or because they want to use them for scientific fitness. At the same time, many non-invasive static human data acquisition devices have become commonplace, and many hospitals and communities are already using them. The smart health system that we designed can easily process the in-exercise and non-exercise data collected by the above two ways. The system, which includes a mobile application, website, cloud server, smart wearable device, and other platforms, generates personalized prescriptions for users by learning the data. The system also includes many functions for public health, and exercise prescription is just one module of it. Using absolute intensity methods (metabolic equivalent of energy (METs), etc.) to determine exercise intensity in exercise prescriptions is straightforward and convenient. Using relative intensity methods (heart rate reserve (%HRR), maximal heart rate (%HRmax), etc.) is more recommended because it is more personalized. Taking target heart rate (THR) given by the relative method as an example, compared with just presenting the THR value, intuitively providing the setting parameters for achieving the THR with specific sport equipment is more user-friendly. The objective of this study was to find a method which combines the advantages (convenient and personalized) of the absolute and relative methods and relatively avoids their disadvantages, helping individuals to meet the target intensity by simply setting equipment parameters. For this purpose, we recruited 32 males and 29 females to undergo incremental cardiopulmonary exercise testing with cycling equipment. The linear regression model of heart rate and exercise wattage (the setting parameter of the equipment) was constructed for each one (R-2 = 0.933, p < 0.001), and the slopes of the graph of these models were obtained. Next, we used an iterative algorithm to obtain a multiple regression model (adjusted R-2 = 0.8336, p < 0.001) of selected static body data and the slopes of participants. The regression model can accurately predict the slope of the general population through their static body data. Moreover, other populations can guarantee comparable accuracy by using questionnaire data for calibration. Then, the predicted slope can be utilized to calculate the equipment's settings for achieving a personalized THR through our equation. All of these steps can be assigned to the intelligent system.
关键词	exercise prescriptions sports equipment exercise intensity intelligent system machine learning
DOI	10.3390/app10217688
关键词[WOS]	BONE-MINERAL DENSITY ; PHYSICAL-ACTIVITIES ; BODY-COMPOSITION ; CARDIORESPIRATORY FITNESS ; MUSCLE STRENGTH ; ACTIVITY CODES ; PRESCRIPTION ; COMPENDIUM ; CAPACITY ; ADULTS
收录类别	SCI
语种	英语
资助项目	Anhui Science and Technology Department[18030801133] ; Science and Technology Service Network Initiative[KU-STS-ZDTP-079] ; National Natural Science Foundation of universities in Anhui Province[KJ2020A0112]
项目资助者	Anhui Science and Technology Department ; Science and Technology Service Network Initiative ; National Natural Science Foundation of universities in Anhui Province
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000589031800001
出版者	MDPI
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/105137
专题	中国科学院合肥物质科学研究院
通讯作者	Sun, Yining
作者单位	1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Anhui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei 230031, Peoples R China 3.Anhui Jianzhu Univ, Sch Elect & Informat Engn, Hefei 230601, Peoples R China 4.Chuzhou Univ, Sch Comp & Informat Engn, Chuzhou 239000, Peoples R China 5.HeFei Normal Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China
推荐引用方式 GB/T 7714	Wu, Yichen,Ma, Zuchang,Zhao, Huanhuan,et al. Achieve Personalized Exercise Intensity through an Intelligent System and Cycling Equipment: A Machine Learning Approach[J]. APPLIED SCIENCES-BASEL,2020,10.
APA	Wu, Yichen,Ma, Zuchang,Zhao, Huanhuan,Li, Yibing,&Sun, Yining.(2020).Achieve Personalized Exercise Intensity through an Intelligent System and Cycling Equipment: A Machine Learning Approach.APPLIED SCIENCES-BASEL,10.
MLA	Wu, Yichen,et al."Achieve Personalized Exercise Intensity through an Intelligent System and Cycling Equipment: A Machine Learning Approach".APPLIED SCIENCES-BASEL 10(2020).