Automatic detection of collisions in elite level rugby union using a wearable sensing device

Sports Engineering

Volumn 15, Issue 2, 2012, Pages 81-92

  Kelly, Daniel ^a   Coughlan, Garrett F ^b   Green, Brian S ^b   Caulfield, Brian ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b IRISH RUGBY FOOTBALL UNION   (Ireland)

Author keywords

Injury prevention; Pattern recognition; Rugby tackle

Indexed keywords

AUTOMATIC DETECTION; FALSE NEGATIVES; FALSE POSITIVE; INJURY PREVENTION; LATEST TECHNOLOGY; MANUAL ANALYSIS; MODELING TECHNIQUE; MONITORING TECHNOLOGIES; PHYSICAL DEMAND; REAL TIME; RECALL AND PRECISION; REHABILITATION STRATEGY; RUGBY TACKLE; SENSING DATA; SENSING TECHNOLOGY; SENSOR DATA; VIDEO FOOTAGE; WEARABLE SENSING;

PATTERN RECOGNITION; SENSORS;

RATING;

EID: 84861770945     PISSN: 13697072     EISSN: 14602687     Source Type: Journal    
DOI: 10.1007/s12283-012-0088-5     Document Type: Article

References (26)

1. Fuller CW, Brooks JHM, Cancea RJ, Hall J, Kemp SPT (2007) Contact events in rugby union and their propensity to cause injury. Br J Sports Med 41(12): 862-867 (discussion 867).
2. Garraway W, Lee A, Macleod D, Telfer J, Deary I, Murrat G (1999) Factors influencing tackle injuries in rugby union football. Br J Sports Med 33: 37-41.
3. Takarada Y (2003) Evaluation of muscle damage after a rugby match with special reference to tackle plays. Br J Sports Med 37(5): 416-419.
4. Barris S, Button C (2008) A review of vision-based motion analysis in sport. Sports Med 38(12): 1025-1043.
5. Fuller CW, Molloy MG, Bagate C, Bahr R, Brooks JHM, Donson H et al (2007) Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union. Br J Sports Med 41(5): 328-331.
6. Hughes M, Franks I (2005) Analysis of passing sequences, shots and goals in soccer. J Sports Sci 23(5): 509-514.
7. Coughlan G, Green B, Pook P, Toolan E, O'Connor S (2011) The relationship between physical game demands and injury rehabilitation in international rugby union: a global positioning system analysis. Br J Sports Med 45(4): 323.
8. Cunniffe B, Proctor W, Baker JS, Davies B (2009) An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. J Strength Cond Res 23(4): 1195.
9. Barbero-Álvarez JC, Coutts A, Granda J, Barbero-Álvarez V, Castagna C (2010) The validity and reliability of a global positioning satellite system device to assess speed and repeated sprint ability (RSA) in athletes. J Sci Med Sport 13(2): 232-235.
10. Coutts A, Duffield R (2010) Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport 13(1): 133-135.
11. Lau HY, Tong KY, Zhu H (2009) Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Human Mov Sci 28(4): 504-514.
12. Pfeiffer M, Hohmann A (2011) Applications of neural networks in training science. Human Mov Sci [Epub ahead of print].
13. Silva A, Costa A, Oliveira P, Reis V (2007) The use of neural network technology to model swimming performance. J Sports Sci Med 6: 117-125.
14. Jäger J (2011) Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons. Human Mov Sci [Epub ahead of print].
15. Maier KD, Wank V, Bartonietz K, Blickhan R (2000) Neural network based models of javelin flight: prediction of flight distances and optimal release parameters. Sports Eng 3(1): 57-63.
16. Lamb PF, Bartlett RM, Robins A (2011) Artificial neural networks for analyzing inter-limb coordination: the golf chip shot. Human Mov Sci 30(6): 1129-1143.
17. Billing DC, Nagarajah CR, Hayes JP, Baker J (2006) Predicting ground reaction forces in running using micro-sensors and neural networks. Sports Eng 9(1): 15-27.
18. Lee M, Roan M, Smith B, Lockhart TE (2009) Gait analysis to classify external load conditions using linear discriminant analysis. Human Mov Sci 28(2): 226-235.
19. Janssen D, Schöllhorn WI, Newell KM, Jäger JM, Rost F, Vehof K (2010) Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps. Human Mov Sci 30(5): 966-975.
20. Chang C, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3): 1-27.
21. Wang SB, Quattoni A, Morency LP, Demirdjian D, Darrell T (2006) Hidden conditional random fields for gesture recognition. Proc IEEE Comput Vis Pattern Recognit 2: 1521-1527.
22. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings of the IEEE, pp 257-286.
23. Kelly D, Mc Donald J, Markham C (2011) Weakly supervised training of a sign language recognition system using multiple Instance learning density matrices. IEEE Trans Syst Man Cybernet Part B (Cybernetics) 41(2): 526-541.
24. Freund Y, Schapire R (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55: 119-139.
25. Valentini G, Dietterich T (2004) Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. J Mach Learn Res 5: 725-775.
26. Brooks JHM, Kemp SPT (2010) Injury-prevention priorities according to playing position in professional rugby union players. Br J Sports Med 45(10): 765-775.