A Method for Cricket Bowling Action Classification and Analysis Using a System of Inertial Sensors

  • Saad Qaisar
  • Sahar Imtiaz
  • Paul Glazier
  • Fatima Farooq
  • Amna Jamal
  • Wafa Iqbal
  • Sungyoung Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7971)

Abstract

A number of similar structured wireless sensors, constituting a Wireless Sensor Network (WSN) are used for activity recognition— particularly for coaching of a bowler to practice correct bowling action in the game of cricket. Several experiments are conducted for training certain algorithms, like K-means and Hidden Markov Model, etc., and the real-time data acquired by a subject under study, or a cricket bowler, is tested for statistical characteristics’ comparison. This paper explains the whole implemented system and the prime application in which it can assist in the field of cricket.

components

WSN Bluetooth K-means MM HMM Cricket Sports biomechanics 

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References

  1. 1.
    Casas, R., Gracia, H.J., Marco, A., Falco, J.L.: Synchronization in Wireless Sensor Networks Using Bluetooth. In: The Third International Workshop on Intelligent Solutions in Embedded Systems Hamburg University of Technology, Hamburg, May 20 (2005)Google Scholar
  2. 2.
    Lu, C., Fu, L.: Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home. IEEE Transactions on Automation Science and Engineering 6(4), 598–609 (2009)CrossRefGoogle Scholar
  3. 3.
    Rennie, J., Press, M.: The computer in the 21st century, Scientific Amer. Special Issue (The Computer in the 21st Century), pp. 4–9 (1995)Google Scholar
  4. 4.
    Zia Uddin, M., et al.: Human Activity Recognition via 3-D joint angle features and Hidden Markov models. In: 17th IEEE International Conference on Image Processing, September 26-29 (2010)Google Scholar
  5. 5.
    Akay, M.: Intelligent Wearable Monitor Systems and Methods. US Patent number 2005/0240086 (October 27, 2005)Google Scholar
  6. 6.
    Weaver, et al.: Apparatus and Method for Processing Data Collected via Wireless Network Sensors. US Patent number 2011/0035271 (February 10, 2011)Google Scholar
  7. 7.
    Kan, Y.-C., Chen, C.-K.: A Wearable Inertial Sensor Node for Body Motion Analysis. IEEE Sensors Journal 12(3), 651–657 (2012)CrossRefGoogle Scholar
  8. 8.
    Aerts, S.G.E.: Wearable Device. US Patent number 2007/0161874 (July 12, 2007)Google Scholar
  9. 9.
    Wixted, A., Spratford, W., Davis, M., Portus, M., James, D.: Wearable Sensors for on Field near Real Time Detection of Illegal Bowling Actions. In: Conference Proceedings for Conference of Science, Medicine & Coaching in Cricket, Sheraton Mirage Gold Coast, Queensland, Australia, June 1-3, pp. 165–168 (2010)Google Scholar
  10. 10.
    Gaffney, M., O’Flynn, B., Mathewson, A., Buckley, J., Barton, J., Angove, P., Vcelak, J.Ó., Conaire, C., Healy, G., Moran, K., O’Connor, N.E., Coyle, S., Kelly, P., Caulfield, B., Conroy, L.: Wearable wireless inertial measurement for sports applications. In: Proc. IMAPS-CPMT Poland 2009, Gliwice – Pszczyna, Poland, September 22-24 (2009)Google Scholar
  11. 11.
    Wixted, A., James, D., Portus, M.: Inertial Sensor Orientation for Cricket Bowling Monitoring. IEEE Sensors, 1835–1838 (October 28-31, 2011)Google Scholar
  12. 12.
    Cheng, L., Hailes, S.: Analysis of Wireless Inertial Sensing for Athlete Coaching Support. In: IEEE Global Telecommunications Conference ‘IEEE GLOBECOM’ 2008, November 30-December 4 (2008)Google Scholar
  13. 13.
    Hon, T.M., Senanayake, S.M.N.A., Flyger, N.: Biomechanical Analysis of 10-Pin Bowling Using Wireless Inertial Sensor. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, July 14-17 (2009)Google Scholar
  14. 14.
    Ghasemzadeh, H., Jafari, R.: Coordination Analysis of Human Movements with Body Sensor Networks: A Signal Processing Model to Evaluate Baseball Swings. IEEE Sensors Journal 11(3), 603–610 (2011)CrossRefGoogle Scholar
  15. 15.
    Guenterberg, E., Yang, A.Y., Ghasemzadeh, H., Jafari, R., Bajcsy, R., Sastry, S.S.: A Method for Extracting Temporal Parameters Based on Hidden Markov Models in Body Sensor Networks With Inertial Sensors. IEEE Transactions on Information Technology In Biomedicine 13(6), 1019–1030 (2009)CrossRefGoogle Scholar
  16. 16.
    ICC Regulations For The Review of Bolwers Reports With Suspected Illegal Bowling Actions, accessed from ICC website link: http://static.icc-cricket.yahoo.net/ugc/documents/DOC_C26C9D9E63C44CBA392505B49890B5AF_1285831722391_859.pdf
  17. 17.
    Guenterberg, E., Ghasemzadeh, H., Jafari, R.: A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks. In: The Sixth International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2009, June 3-5 (2009)Google Scholar
  18. 18.
    Aminian, K., Najafi, B.: Capturing human motion using bodyfixed sensors: outdoor measurement and clinical applications. Computer Animation and Virtual Worlds 15(2), 79–94 (2004)CrossRefGoogle Scholar
  19. 19.
    http://www.bluetooth.com/Pages/Basics.aspx (accessed on May 10, 2012)
  20. 20.
    Casas, R., et al.: Synchronization in Wireless Sensor Networks Using Bluetooth. In: The Third International Workshop on Intelligent Solutions in Embedded Systems, May 20. Hamburg University of Technology, Hamburg (2005)Google Scholar
  21. 21.
    Wang, D., et al.: A Wireless Sensor Network Based on Bluetooth for Telemedicine Monitoring System. In: The Proceedings of IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (2005)Google Scholar
  22. 22.
    Ghasemzadeh, H., Loseu, V., Jafari, R.: Collaborative signal processing for action recognition in body sensor networks: A distributed classification algorithm using motion transcripts. In: International Conference on Information Processing in Sensor Networks, IPSN 2010 (2010)Google Scholar
  23. 23.
    Sutton, C., McCallum, A.: An Introduction to Conditional Random Fields for Relational Learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press (2006)Google Scholar
  24. 24.
    Kim, E., Helal, S., Cook, D.: Human Activity Recognition and Pattern Discovery. IEEE Pervasive Computing 9(1), 48–53 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Saad Qaisar
    • 1
  • Sahar Imtiaz
    • 1
  • Paul Glazier
    • 2
  • Fatima Farooq
    • 1
  • Amna Jamal
    • 1
  • Wafa Iqbal
    • 1
  • Sungyoung Lee
    • 3
  1. 1.School of Electrical Engineering & Computer ScienceNational University of Science & TechnologyIslamabadPakistan
  2. 2.Sheffield Hallam UniversityUnited Kingdom
  3. 3.Kyung Hee UniversityKorea

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