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Design of an Intelligent Acquisition System for Athletes’ Physiological Signal Data Based on Internet of Things Cloud Computing

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Abstract

The application background of physiological signal research focuses on education and research in the fields of life sciences, clinical diagnosis and monitoring applications, and is an important content in the field of biomedical engineering. The development and use of intelligent physiological signal acquisition systems have also become the current life science research One of the hot spots. The acquisition and processing of physiological and medical signals is an important technology in biomedical engineering, which provides a necessary benchmark for the research of sports physiology and medicine. This article aims to provide some ideas and directions for the research on collecting physiological signal data of athletes under the cloud computing environment and the application of Internet of Things technology.and mainly introduces the design of the intelligent acquisition system of physiological signal data of athletes under cloud computing. This paper proposes an intelligent acquisition method for athletes’ physiological signal data under cloud computing, including the design of power frequency filter and the energy model of the TDMA protocol system, which is used to conduct research experiments on the design of an athlete’s physiological signal data intelligent acquisition system under cloud computing. The experimental results of this article show that the average collection accuracy rate of the system is 97.01%, the test of the intelligent acquisition system shows that the stability of the system is very high, and the accuracy rate is high, which can better collect the physiological signal data of athletes.

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References

  1. Chen X, Jiao L, Li W, Fu X (2016) Efficient multi-user computation offloading for Mobile-edge cloud computing. IEEE/ACM Trans Networking 24(5):2795–2808

    Article  Google Scholar 

  2. Zhang R, Wu K, Li M, Wang J (2016) Online resource scheduling under concave pricing for cloud computing. IEEE Trans Parallel Distrib Syst 27(4):1131–1145

    Article  Google Scholar 

  3. Yan Q, Yu R, Gong Q et al (2016) Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: a survey, some research issues, and challenges. IEEE Commun Surv Tutor 18(1):602–622

    Article  Google Scholar 

  4. Abbas H, Maennel O, Assar S (2017) Security and privacy issues in cloud computing. Ann Telecommun 72(5–6):233–235

    Article  Google Scholar 

  5. Awodele O, Izang AA, Kuyoro SO et al (2016) Big data and cloud computing issues. Appl Radiol 3(12):1647–1648

    Google Scholar 

  6. Datta S, Bettinger K, Snyder M (2016) Corrigendum: secure cloud computing for genomic data. Nat Biotechnol 34(10):588–591

    Article  Google Scholar 

  7. Paranjothi A, Khan MS, Nijim M (2017) Survey on three components of Mobile cloud computing: offloading, distribution and privacy. J Comput Commun 5(6):1–31

    Article  Google Scholar 

  8. Napoli C, Pappalardo G, Tina GM, Tramontana E (2016) Cooperative strategy for optimal Management of Smart Grids by wavelet RNNs and cloud computing. IEEE Trans Neural Netw Learn Syst 27(8):1672–1685

    Article  MathSciNet  Google Scholar 

  9. Baldassarre MT, Caivano D, Dimauro G, Gentile E, Visaggio G (2018) Cloud computing for education: a systematic mapping study. IEEE Trans Educ 61(3):234–244

    Article  Google Scholar 

  10. Wang Y, Meng S, Chen Y et al (2017) Multi-leader multi-follower Stackelberg game based dynamic resource allocation for Mobile cloud computing environment. Wirel Pers Commun 93(2):1–20

    Article  Google Scholar 

  11. Singh P, Dutta M, Aggarwal N (2017) A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl Inf Syst 52(1):1–51

    Article  Google Scholar 

  12. Kaiping X, Jianan H, Yongjin M et al (2018) Fog-aided verifiable privacy preserving access control for latency-sensitive data sharing in vehicular cloud computing. IEEE Netw 32(3):7–13

    Article  Google Scholar 

  13. Mazza D, Tarchi D, Corazza GE (2017) A unified urban Mobile cloud computing offloading mechanism for smart cities. IEEE Commun Mag 55(3):30–37

    Article  Google Scholar 

  14. Ibtihal M, Driss EO, Hassan N (2017) Homomorphic encryption as a Service for Outsourced Images in Mobile cloud computing environment. Intl J Cloud Appl Comput 7(2):27–40

    Google Scholar 

  15. Mahir K (2016) Altan, et al. an adaptive mobile cloud computing framework using a call graph based model. J Netw Comput Appl 65(Apr.):12–35

    Google Scholar 

  16. Rezaei H, Karimi B, Hosseini SJ (2016) Effect of cloud computing Systems in Terms of service quality of knowledge management systems. Lecture Notes Softw Eng 4(1):73–76

    Article  Google Scholar 

  17. Yu J, Xiao X, Zhang Y (2016) From concept to implementation: the development of the emerging cloud computing industry in China. Telecommun Policy 40(2–3):130–146

    Article  Google Scholar 

  18. Liu Y, Xiao F (2021) Intelligent Monitoring System of Residential Environment based on Cloud Computing and Internet of things. IEEE Access, PP(99):1–1

  19. Okada G, Yonezawa T, Kurita K, Tsumura N (2018) [paper] monitoring emotion by remote measurement of physiological signals using an RGB camera. Ite Trans Media Technol Appl 6(1):131–137

    Article  Google Scholar 

  20. Moon SE, Lee JS (2017) Perceptual experience analysis for tone-mapped HDR videos based on EEG and peripheral physiological signals. IEEE Trans Auton Ment Dev 7(3):236–247

    Article  Google Scholar 

  21. Krupa N, Anantharam K, Sanker M, Datta S, Sagar JV (2016) Recognition of emotions in autistic children using physiological signals. Health & Technology 6(2):137–147

    Article  Google Scholar 

  22. Tripathy RK, Deb S, Dandapat S (2017) Analysis of physiological signals using state space correlation entropy. Healthcare Technol Lett 4(1):30–33

    Article  Google Scholar 

  23. Mohanavelu K, Lamshe R, Poonguzhali S, Adalarasu K, Jagannath M (2017) Assessment of human fatigue during physical performance using physiological signals: a review. Biom Pharmacol J 10(4):1887–1896

    Article  Google Scholar 

  24. Couper MP (2017) The future of modes of data collection. Pub Opinion Quart 75(5):889–908

    Article  Google Scholar 

  25. Dong M, Ota K, Liu A (2017) RMER: reliable and energy-efficient data collection for large-scale wireless sensor networks. IEEE Internet Things J 3(4):511–519

    Article  Google Scholar 

  26. Tamaki N, Mukai T, Ishii Y et al (2017) Comparative study of thallium emission myocardial tomography with 180° and 360° data collection. J Nucl Med 23(8):661–666

    Google Scholar 

  27. Price L, Reilly J, Godwin J, Cairns S, Hopkins S, Cookson B, Malcolm W, Hughes G, Lyytikäinen O, Coignard B, Hansen S (2016) A cross-sectional survey of the acceptability of data collection processes for validation of a European point prevalence survey of healthcare-associated infections and antimicrobial use. J Infect Prev 17(3):122–126

    Article  Google Scholar 

  28. Jameson A, Gajos K (2017) ACM transactions on interactive intelligent systems (TiiS). J Assoc Comput Mach 63(3):304–304

    Google Scholar 

  29. Charles L (2017) Conditional inference and logic for intelligent systems. J Oper Res Soc 44(1):87–88

    Google Scholar 

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Authors and Affiliations

  1. Physical Education Department, ZhengZhou Institute of Finance and Economics, Zhengzhou, 450000, Henan, China

    Kai Jiang

  2. Sports Reform and Development Research Center, Institute of physical education, Henan University, Kaifeng, 475001, Henan, China

    Yuntao Zhou

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  1. Kai Jiang
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  2. Yuntao Zhou
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Correspondence to Yuntao Zhou.

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Jiang, K., Zhou, Y. Design of an Intelligent Acquisition System for Athletes’ Physiological Signal Data Based on Internet of Things Cloud Computing. Mobile Netw Appl 27, 836–847 (2022). https://doi.org/10.1007/s11036-021-01810-9

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  • DOI: https://doi.org/10.1007/s11036-021-01810-9

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