SpringerLink
Log in

The construction of national fitness online platform system under mobile internet technology

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

With the rapid development of the mobile Internet, people's demand for information is increasing, and the traditional fitness model is unable to meet the development needs of society. In this study, mobile Internet technology is used to build a new type of green intelligent fitness system. The system can collect users’ fitness data and upload the data to the cloud server. And users can obtain their exercise data and their ranks at any time through mobile APP to realize data sharing. At the same time, WebSocket technology is used to realize real-time updates of data, and a collaborative filtering recommendation algorithm is used to analyze users’ rating data and recommend intelligent fitness equipment for users. It is found that the system constructed in this study uses the computing power of multiple nodes in the cluster to analyze the fitness data on the cluster rapidly. Based on the collaborative filtering algorithm, the analysis of users is realized, and the recommendation accuracy is up to 89%. This study first puts forward the combination of mobile Internet and traditional fitness industry, which provides a reliable way to promote the development of national fitness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abualigah L., Diabat A. Advances in sine cosine algorithm: a comprehensive survey. Artificial Intelligence Review, 2021, 1–42

  • Abualigah L, Yousri D, Abd EM, Ewees AA, Al-qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250–107256

    Article  Google Scholar 

  • Anacleto S, Mota P, Fernandes V, Carvalho N, Morais N, Passos P et al (2021) Can narration and guidance in video-enhanced learning improve performance on E-BLUS exercises? Central European Journal of Urology 74(1):131–136

    Google Scholar 

  • Animaw W, Seyoum Y (2017) Increasing prevalence of diabetes mellitus in a develo** country and its related factors. PLoS ONE 12(11):e0187670–e0187676

    Article  Google Scholar 

  • Barkley JE, Lepp A, Santo A, Glickman E, Dowdell B (2020) The relationship between fitness app use and physical activity behavior is mediated by exercise identity. Comput Hum Behav 108:106313–106321

    Article  Google Scholar 

  • Cai J., Zhao Y., Sun J. Factors Influencing Fitness App Users’ Behavior in China. International Journal of Human–Computer Interaction, 2021, 1–11

  • Dancy E, Garfall AL, Cohen AD, Fraietta JA, Davis M, Levine BL et al (2018) Clinical predictors of T cell fitness for CAR T cell manufacturing and efficacy in multiple myeloma. Blood 132(Supplement 1):1886–1891

    Article  Google Scholar 

  • de Luna IR, Montoro-Ríos F, Liébana-Cabanillas F, de Luna JG (2017) NFC technology acceptance for mobile payments: a Brazilian perspective. Revista Brasileira De Gestão De Negócios 19(63):82–94

    Google Scholar 

  • Emara TZ, Huang JZ (2019) RRPlib: a spark library for representing HDFS blocks as a set of random sample data blocks. Sci Comput Program 184:102301–102311

    Article  Google Scholar 

  • Feng W, Zhu Q, Zhuang J, Yu S (2019) An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth. Clust Comput 22(3):7401–7412

    Article  Google Scholar 

  • Tehranipoor F., Karimian N., Wortman P.A., Chandy J.A., editors. Low-cost authentication paradigm for consumer electronics within the internet of wearable fitness tracking applications. ICCE; 2018,114–121

  • Fühner T, Kliegl R, Arntz F, Kriemler S, Granacher U (2021) An update on secular trends in physical fitness of children and adolescents from 1972 to 2015: a systematic review. Sports Medicine (auckland, Nz) 51(2):303–313

    Article  Google Scholar 

  • Grundy Q, Held F, Bero L (2017) A social network analysis of the financial links backing health and fitness apps. Am J Public Health 107(11):1783–1788

    Article  Google Scholar 

  • Guo X, Liu J, Shi C, Liu H, Chen Y, Chuah MC (2018) Device-free personalized fitness assistant using WiFi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2(4):1–23

    Article  Google Scholar 

  • Gyrard A, Sheth A (2020) IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness. Smart Health 15:100083–100091

    Article  Google Scholar 

  • Harder H, Holroyd P, Burkinshaw L, Watten P, Zammit C, Harris PR et al (2017) A user-centred approach to develo** bWell, a mobile app for arm and shoulder exercises after breast cancer treatment. J Cancer Surviv 11(6):732–742

    Article  Google Scholar 

  • Hock J, Reiner B, Neidenbach RC, Oberhoffer R, Hager A, Ewert P et al (2018) Functional outcome in contemporary children with total cavopulmonary connection–Health-related physical fitness, exercise capacity and health-related quality of life. Int J Cardiol 255:50–54

    Article  Google Scholar 

  • Huang G., Zhou E. Time to work out! Examining the behavior change techniques and relevant theoretical mechanisms that predict the popularity of fitness mobile apps with Chinese-language user interfaces. Health communication, 2018, 114–121

  • Jiang L, Cheng Y, Yang L, Li J, Yan H, Wang X (2019) A trust-based collaborative filtering algorithm for E-commerce recommendation system. J Ambient Intell Humaniz Comput 10(8):3023–3034

    Article  Google Scholar 

  • Johnson BT, Acabchuk RL (2018) What are the keys to a longer, happier life? Answers from five decades of health psychology research. Soc Sci Med 196:218–226

    Article  Google Scholar 

  • Kandhway P, Bhandari AK, Singh A (2020) A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization. Biomed Signal Process Control 56:101677–101681

    Article  Google Scholar 

  • Karabadji NEI, Beldjoudi S, Seridi H, Aridhi S, Dhifli W (2018) Improving memory-based user collaborative filtering with evolutionary multi-objective optimization. Expert Syst Appl 98:153–165

    Article  Google Scholar 

  • Kildare CA, Middlemiss W (2017) Impact of parents mobile device use on parent-child interaction: a literature review. Comput Hum Behav 75:579–593

    Article  Google Scholar 

  • Klesmith JR, Hackel BJ (2019) Improved mutant function prediction via PACT: protein analysis and classifier toolkit. Bioinformatics 35(16):2707–2712

    Article  Google Scholar 

  • Li Y-M, Han J, Liu Y, Wang R, Wang R, Wu X-P et al (2019) China survey of fitness trends for 2020. Acsm’s Health & Fitness Journal 23(6):19–27

    Article  Google Scholar 

  • Li A, Sun Y, Guo X, Guo F, Guo J (2021) Understanding how and when user inertia matters in fitness app exploration: A moderated mediation model. Inf Process Manag 58(2):102458

    Article  Google Scholar 

  • Meng X, Li Z, Wang S, Karambakhsh A, Sheng B, Yang P et al (2020) A video information driven football recommendation system. Comput Electr Eng 85:106699–106706

    Article  Google Scholar 

  • Pellizzari Cid G.F. Evaluación de factibilidad técnico, económica y estratégica de una aplicación móvil para aprovechar la oferta de gimnasios. 2020,124–131

  • Raghuveer G, Hartz J, Lubans DR, Takken T, Wiltz JL, Mietus-Snyder M et al (2020) Cardiorespiratory fitness in youth: an important marker of health: a scientific statement from the American heart association. Circulation 142(7):e101–e118

    Article  Google Scholar 

  • Reda R., Carbonaro A., editors. Design and Development of a Linked Open Data-Based Web Portal for Sharing IoT Health and Fitness Datasets. Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good; 2018, 142–153

  • Rodriguez G, Rocha FG (2018) Revising frameworks for develo** mobile virtual reality. Interfaces Científicas-Exatas e Tecnológicas 3(2):35–48

    Article  Google Scholar 

  • Serrano KJ, Thai CL, Greenberg AJ, Blake KD, Moser RP, Hesse BW (2017) Progress on broadband access to the Internet and use of mobile devices in the United States: tracking healthy people 2020 goals. Public Health Rep 132(1):27–31

    Article  Google Scholar 

  • Shen Y., editor An Empirical Study on the Influential Factors of User Loyalty in Digital Fitness Community. International Conference on Human-Computer Interaction; 2019,1136–1141

  • Tang Y, Wang D (2020) Optimization of sports fitness management system based on internet of health things. IEEE Access 8:209556–209569

    Article  Google Scholar 

  • Wang J, Lv B (2019) Big data analysis and research on consumption demand of sports fitness leisure activities. Clust Comput 22(2):3573–3582

    Article  Google Scholar 

  • Xu YP, Tan JW, Zhu DJ, Ouyang P, Taheri B (2021) Model identification of the proton exchange membrane fuel cells by extreme learning machine and a developed version of arithmetic optimization algorithm. Energy Rep 7:2332–2342

    Article  Google Scholar 

Download references

Funding

This work was supported by Philosophy and social science planning project of Guangdong Province, Approval No.: GD17XTY13.

Author information

Authors and Affiliations

  1. Ministry of Public Foundation, Zhaoqing Medical College, Zhaoqing, 526020, Guangdong, China

    **aojun Liang

  2. Graduate School, University of Perpetual Help System DALTA, 1740, Las Piñas City, Manila, Philippines

    **aojun Liang

  3. School of Management, Guang Dong AIB Polytechnic, Guangzhou, 510507, Guangdong, China

    **n Kuang

  4. Ministry of Basic Education, Guangdong Eco-Engineering Polytechnic, Guangzhou, 510520, China

    Yi Xu

  5. Department of Computer and Software Engineering, Wonkwang University, Iksan, 54538, Korea

    Haibin **ao

Authors
  1. **aojun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **n Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haibin **ao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to **n Kuang.

Ethics declarations

Conflict of interest

All Authors declare that they have no conflict of interest.

Human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, X., Kuang, X., Xu, Y. et al. The construction of national fitness online platform system under mobile internet technology. Int J Syst Assur Eng Manag 14, 98–109 (2023). https://doi.org/10.1007/s13198-021-01198-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-021-01198-5

Keywords

Search

Navigation