• SAAD QAISAR School of Electrical Engineering & Computer Science, National University of Sciences & Technology, Islamabad, Pakistan
  • SAHAR IMTIAZ School of Electrical Engineering & Computer Science, National University of Sciences & Technology, Islamabad, Pakistan
  • FATMA FARUQ School of Electrical Engineering & Computer Science, National University of Sciences & Technology, Islamabad, Pakistan
  • PAUL GLAZIER Institute of Sport, Exercise & Active Living, Victoria University, Footscray Park Campus, Melbourne, VIC 8801, Australia
  • SUNGYOUNG LEE Kyung Hee University, Korea


Hidden Markov Model, Cricket, Action Classification


Hidden Markov Models (HMM) have been used for to accurately model, detect and classify key phenomenon. In this manuscript, we propose use of HMM for detection and classification of arm action in the game of cricket. The technique uses sensor data gathered from wearable sensors placed at wrist, elbow and shoulder. The sensor data consists of both displacement and rotational information collected through a combination of accelerometer and gyroscope placed at each joint. A Bluetooth transceiver is attached to the arm in order to wirelessly transfer the gathered data to the base station. A K-means clustering algorithm classifies the current position and angular rotation of the joint for each of the sensor placements. A Markov chain then determines the chain of sequence for a set of joint movements (displacement and angular rotation) to classify it as a specific arm motion. A Hidden Markov Model determines the previous state of arm motion in order to classify the current state and hence, the current action since the movements happen in progression, when following the other. Experiments show an accuracy of up to 100% in correctly determining the arm action against a model built around a trace-set collected from a sports biomechanics expert. The proposed model has applications in cricket coaching and technique adaptation both for novice and trained players.



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R. Casas, H. J. Gracia, A. Marco, J. L. Falco; ”Synchronization in Wireless Sensor Networks Using Bluetooth” In The Third

International Workshop on Intelligent Solutions in Embedded Systems Hamburg University of Technology, Hamburg, May


C. Lu, L. Fu; “Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home”, IEEE

Transactions on Automation Science and Engineering, vol. 6, no. 4, pp 598 – 609, October 2009.

J. Rennie and M. Press, “The computer in the 21st century”, Scientific Amer. Special Issue (The Computer in the 21st

Century), pp. 4–9, 1995.

M. Zia Uddin et. al.; “Human Activity Recognition via 3-D joint angle features and Hidden Markov models”; In 17th IEEE

International Conference on Image Processing; 26-29 Sept. 2010.

M. Akay, “Intelligent Wearable Monitor Systems and Methods”, US Patent number 2005/0240086, October 27, 2005.

Weaver et. al., “Apparatus and Method for Processing Data Collected via Wireless Network Sensors”, US Patent number

/0035271, February 10, 2011.

Yao-Chiang Kan; Chun-Kai Chen; , "A Wearable Inertial Sensor Node for Body Motion Analysis," Sensors Journal, IEEE ,

vol.12, no.3, pp.651-657, March 2012.

S. G. E. Aerts, “Wearable Device”, US Patent number 2007/0161874, July 12, 2007.

A. Wixted, W. Spratford, M. Davis, M. Portus, D. James, “Wearable Sensors for on Field near Real Time Detection of

Illegal Bowling Actions”, Conference Proceedings for Conference of Science, Medicine & Coaching in Cricket, Sheraton

Mirage Gold Coast, Queensland, Australia, 1-3 June 2010, pp. 165-168.

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. 2009. “Wearable wireless inertial measurement for sports

applications”. Proc. IMAPS-CPMT Poland 2009, Gliwice – Pszczyna, Poland, 22-24 Sept, 2009

A. Wixted, D. James, M. Portus, “Inertial Sensor Orientation for Cricket Bowling Monitoring”, IEEE Sensors, pp. 1835-

, 28-31 October, 2011.

L. Cheng, S. Hailes, “Analysis of Wireless Inertial Sensing for Athlete Coaching Support”, IEEE Global

Telecommunications Conference ‘IEEE GLOBECOM’ 2008, Nov. 30-Dec. 4, 2008.

T. M. Hon, S. M. N. A. Senanayake, N. Flyger, “Biomechanical Analysis of 10-Pin Bowling Using Wireless Inertial

Sensor”, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, July 14-17, 2009.

H. Ghasemzadeh and R. Jafari. s.l.; “Coordination Analysis of Human Movements with Body Sensor Networks: A Signal

Processing Model to Evaluate Baseball Swings”; Sensors Journal, IEEE, vol. 11, no. 3, pp. 603-610, March 2011.

E. Guenterberg, A.Y.Yang, Hassan Ghasemzadeh, R. Jafari, R.Bajcsy, and S. S. Sastry, “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, vol. 13, no. 6, pp. 1019-1030, November 2009.

ICC Regulations For The Review of Bolwers Reports With Suspected Illegal Bowling Actions (accessed from ICC website

in 2013, link: http://static.icccricket.


E. Guenterberg, H. Ghasemzadeh, and R. Jafari, “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, 2009. BSN

, 3-5 June 2009.

K. Aminian and B. Najafi, "Capturing human motion using bodyfixed sensors: outdoor measurement and clinical

applications," Computer Animation and Virtual Worlds, vol. 15, no. 2, pp. 79-94, 2004.

URL: http://www.bluetooth.com/Pages/Basics.aspx, accessed on 10th May, 2012.

R. Casas, et al, “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.

D. Wang, er. 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.

H. Ghasemzadeh, V. Loseu, and R. Jafari; “Collaborative signal processing for action recognition in body sensor networks:

A distributed classification algorithm using motion transcripts,” Information Processing in Sensor Networks; 2010. IPSN

’10. International Conference on, 2010.

C. Sutton and A. McCallum; “An Introduction to Conditional Random Fields for Relational Learning”; L. Getoor and B.

Taskar, eds., Introduction to Statistical Relational Learning, MIT Press, 2006.

E. Kim, S. Helal and D. Cook; “Human Activity Recognition and Pattern Discovery”; IEEE Pervasive Computing,; vol. 9,

no. 1, pp 48-53; Jan.-March 2010.