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

Elite rugby union teams currently employ the latest technology to monitor and evaluate the physical demands of training and games on their players. Tackling has been shown to be the most common cause of injury in rugby union, yet current player monitoring technology does not effectively evaluate player tackling measurements. Currently, to evaluate measurements specific to player tackles, a time-consuming manual analysis of player sensor data and video footage is required. The purpose of this work is to investigate tackle modeling techniques which can be utilised to automatically detect player tackles and collisions using sensing technology already being used by elite international and club level rugby union teams. This paper discusses issues relevant to automatic tackle analysis, describes a technique to detect tackles using sensing data and validates the technique by comparing automatically detected collisions to manually labeled collisions using data from elite club and international level players. The results of the validation show that the system is able to consistently identify collisions with very few false positives and false negatives, achieving a recall and precision rating of 0.933 and 0.958, respectively. The aim is that the automatically detected tackles can provide coaching, medical and strength and conditioning staff with objective tackle-specific measurements, in real time, which can be used in injury prevention and rehabilitation strategies.

[1]  Chris Button,et al.  A Review of Vision-Based Motion Analysis in Sport , 2008, Sports medicine.

[2]  Colin W Fuller,et al.  Contact events in rugby union and their propensity to cause injury , 2007, British Journal of Sports Medicine.

[3]  Karl M Newell,et al.  Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps. , 2011, Human movement science.

[4]  Charles Markham,et al.  Weakly Supervised Training of a Sign Language Recognition System Using Multiple Instance Learning Density Matrices , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[6]  Mike Hughes,et al.  Analysis of passing sequences, shots and goals in soccer , 2005, Journal of sports sciences.

[7]  S. Kemp,et al.  Injury-prevention priorities according to playing position in professional rugby union players , 2010, British Journal of Sports Medicine.

[8]  Reinhard Blickhan,et al.  Neural network based models of javelin flight: prediction of flight distances and optimal release parameters , 2000 .

[9]  Carlo Castagna,et al.  The validity and reliability of a global positioning satellite system device to assess speed and repeated sprint ability (RSA) in athletes. , 2010, Journal of science and medicine in sport.

[10]  Bruce Davies,et al.  An Evaluation of the Physiological Demands of Elite Rugby Union Using Global Positioning System Tracking Software , 2009, Journal of strength and conditioning research.

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  I J Deary,et al.  Factors influencing tackle injuries in rugby union football. , 1999, British journal of sports medicine.

[13]  Aaron J. Coutts,et al.  Validity and reliability of GPS devices for measuring movement demands of team sports. , 2010, Journal of science and medicine in sport.

[14]  Jason P. Hayes,et al.  Predicting ground reaction forces in running using micro-sensors and neural networks , 2006 .

[15]  Wolfgang I Schöllhorn,et al.  Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons. , 2012, Human movement science.

[16]  Thurmon E. Lockhart,et al.  Gait analysis to classify external load conditions using linear discriminant analysis. , 2009, Human movement science.

[17]  Mark Pfeiffer,et al.  Applications of neural networks in training science. , 2012, Human movement science.

[18]  Giorgio Valentini,et al.  Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods , 2004, J. Mach. Learn. Res..

[19]  Y. Takarada.  Evaluation of muscle damage after a rugby match with special reference to tackle plays , 2003, British journal of sports medicine.

[20]  Hailong Zhu,et al.  Support vector machine for classification of walking conditions of persons after stroke with dropped foot. , 2009, Human movement science.

[21]  Trevor Darrell,et al.  Hidden Conditional Random Fields for Gesture Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  Garrett F. Coughlan,et al.  The relationship between physical game demands and injury rehabilitation in international rugby union: a global positioning system analysis , 2011, British Journal of Sports Medicine.

[23]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[24]  Victor Machado Reis,et al.  The use of neural network technology to model swimming performance. , 2007, Journal of sports science & medicine.

[25]  Anthony Robins,et al.  Artificial neural networks for analyzing inter-limb coordination: the golf chip shot. , 2011, Human movement science.

[26]  A S McIntosh,et al.  Consensus Statement on Injury Definitions and Data Collection Procedures for Studies of Injuries in Rugby Union , 2007, Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine.