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Sports Policy Implementation by the IoT Platform

  • Vishnu Priya Reddy EnugalaEmail author
  • M. Abhinava Vinay Kumar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)

Abstract

In India, presently the meritorious sportspersons are not being benefited due to the non-implementation of the latest technologies; this can be achievable with the sports policy using IoT platform. By developing a special web address or a mobile application for each game to have the live telecast matches, an online score of matches with player details and along with online referee support method interlinking these through the internet and store all the information in the cloud accessible only to the sports policy authorities. Within this context, the contribution of this study is (i) Educating player in problem-solving for better performance. (ii) Player healthcare like nutrition, medical supports, fitness. (iii) High quality of digital and visualization technological skillful game training. (iv) Latest referee rules and regulations. (v) Identification of the meritorious sportsperson from the metropolitan area to agency area. This all is monitored to provide sports Excellency facilities and required sports benefits.

Keywords

Internet of things Sports policy Sportsperson benefits Monitoring the sportsperson records Health care activities 

References

  1. 1.
    Fuss, F.K., Lythgo, N., Smith, R.M., Benson, A.C., Gordon, B.: Identification of key performance parameters during off-spin bowling with a smart cricket ball. Sports Technol. 4(3–4), 159–163 (2011)CrossRefGoogle Scholar
  2. 2.
    Fuss, F.K., Smith, R.M.: Accuracy performance parameters of seam bowling, measured with a smart cricket ball. Procedia Eng. 72, 435–440 (2014)CrossRefGoogle Scholar
  3. 3.
    Fuss, F.K., Smith, R.M., Subic, A.: Determination of spin rate and axes with an instrumented cricket ball. Procedia Eng. 34, 128–133 (2012)CrossRefGoogle Scholar
  4. 4.
    Mcginnis, R.S., Perkins, N.C.: A highly miniaturized, wireless inertial measurement unit for characterizing the dynamics of pitched baseballs and softballs. Sensors 12(9), 11933–11945 (2012)CrossRefGoogle Scholar
  5. 5.
    Fuss, F.K., Ferdinands, R., Doljin, B., Beach, A.: Development of a smart cricket ball and advanced performance analysis of spin bowling. In: Advanced Technologies in Modern Day Sports (2014), Institute for Sports Research (ISR). ICSST, pp. 588–595 (2014)Google Scholar
  6. 6.
    King, K., Perkins, N.C., Churchill, H., Mcginnis, R., Doss, R., Hickland, R.: Bowling ball dynamics revealed by miniature wireless MEMs inertial measurement unit. Sports Eng. 13(2), 95–104 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhou, B., Koerger, H., Wirth, M., Zwick, C., Martindale, C., Cruz, H., Eskofier, B., Lukowicz, P.: Smart soccer shoe monitoring football interaction with shoe integrated textile pressure sensor matrix. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers ACM, pp. 64–71 (2016)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    Halvorsen, P., Saegrov, S., Mortensen, A., Kristensen, D.K., Eichhorn, A., Stenhaug, M., Dahl, S., Stensland, H.K., Gaddam, V.R., Griwodz, C., et al.: Bagadus an integrated system for arena sports analytics a soccer case study. In: Proceedings of the 4th ACM Multimedia Systems Conference. ACM, pp. 48–59 (2013)Google Scholar
  11. 11.
    Seo, Y., Choi, S., Kim, H., Hong, K.S.: Where are the ball and players? Soccer game analysis with color-based tracking and image mosaic. In: International Conference on Image Analysis and Processing, Springer, pp. 196–203 (1997)Google Scholar
  12. 12.
    Yu, X., Xu, C., Leong, H.W., Tian, Q., Tang, Q., Wan, K.W.: Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video. In: Proceedings of the Eleventh ACM International Conference on Multimedia. ACM, pp. 11–20 (2003)Google Scholar
  13. 13.
    Bahil, P., Padmanabhan, V.N.: Radar: an in-building RF-based user location and tracking system. In: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM Proceedings IEEE. IEEE, vol. 2, pp. 775–784 (2000)Google Scholar
  14. 14.
    Chintalapudi, K., Padmanabha Iyer, A., Padmanabhan, V. N.: Indoor localization without the pain. In: Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking (2010), ACM, pp. 173–184Google Scholar
  15. 15.
    Kumar, S., Gil, S., Katabi, D., Rus, D.: Accurate indoor localization with zero start-up cost. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (2014), ACM, pp. 483–494Google Scholar
  16. 16.
    Niculescu, D., Nath, B.: Ad hoc positioning system (APS) using AOA. In: Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, INFOCOM, IEEE Societies. IEEE, vol. 3, pp. 1734–1743 (2003)Google Scholar
  17. 17.
    Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., Sen, R.: Zero-effort crowd sourcing for indoor localization. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, ACM, pp. 293–304 (2012)Google Scholar
  18. 18.
    Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., Choudhury, R.R.: No need to war-drive, unsupervised indoor localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. ACM, pp. 197–210 (2012)Google Scholar
  19. 19.
    Xiong, J., Jamieson, K.: Array track a fine-grained indoor location system. In: Presented as Part of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), pp. 71–84 (2013)Google Scholar
  20. 20.
    Youssef, M., Agrawala, A.: The horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications and Services ACM, pp. 205–218 (2005)Google Scholar
  21. 21.
    Lefferts, E.J., Markley, F.L., Shuster, M.D.: Kalman filtering for spacecraft attitude estimation. J. Guid. Control Dyn. 5(5), 417–429 (1982)CrossRefGoogle Scholar
  22. 22.
    Liang W.Y., Miao, W.T., Hong L.J., Lei, X.C., Chen, Z.: Attitude estimation for small helicopter using extended kalman filter. In: 2008 IEEE Conference on Robotics, Automation and Mechatronics. IEEE, pp. 577–581 (2008)Google Scholar
  23. 23.
    Madgwick, S.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. In: Report x-io and University of Bristol, UK (2010)Google Scholar
  24. 24.
    Mathony, R., Hamel, T., Pflimlin, J.M.: Nonlinear complementary filters on the special orthogonal group. IEEE Trans. Autom. Control 53(5), 1203–1218 (2008)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Pflimlin, J.M., Hamel, T., Soueres, P.: Nonlinear attitude and gyroscope’s bias estimation for a VTOL UAV. Int. J. Syst. Sci. 38(3), 197–210 (2007)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Zhou, P., Li, M., Shen, G.: Use it free instantly knowing your phone attitude. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking ACM, pp. 605–616 (2014)Google Scholar
  27. 27.
    Siouris, G.M., Chen, G., Wang, J.: Tracking an incoming ballistic missile using an extended interval kalman filter. IEEE Trans. Aerosp. Electron. Syst. 33(1), 232–240 (1997)CrossRefGoogle Scholar
  28. 28.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vishnu Priya Reddy Enugala
    • 1
    Email author
  • M. Abhinava Vinay Kumar
    • 2
  1. 1.Department of Computer Science and EngineeringCarrier Point UniversityRajasthanIndia
  2. 2.Department of SociologyCarrier Point UniversityRajasthanIndia

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