Classification of team sport activities using a single wearable tracking device

Journal of Biomechanics

Volumn 48, Issue 15, 2015, Pages 3975-3981

  Wundersitz, Daniel W T ^a   Josman, Casey ^b   Gupta, Ritu ^b   Netto, Kevin J ^b   Gastin, Paul B ^a   Robertson, Sam ^a,c  

^a DEAKIN UNIVERSITY   (Australia)

^b CURTIN UNIVERSITY   (Australia)

^c VICTORIA UNIVERSITY   (Australia)

Author keywords

Accelerometer; Gyroscope; Logistic Regression Tree; Random Forest; Support Vector Machine

Indexed keywords

ACCELEROMETERS; DECISION TREES; ECONOMIC AND SOCIAL EFFECTS; FORESTRY; FREQUENCY DOMAIN ANALYSIS; GYROSCOPES; SPORTS; SUPPORT VECTOR MACHINES; TIME DOMAIN ANALYSIS; WEARABLE SENSORS;

CLASSIFICATION ACCURACY; FEATURE SELECTION METHODS; FREQUENCY DOMAINS; LOGISTIC REGRESSION TREES; RANDOM FORESTS; RELEVANT FEATURES; THORACIC VERTEBRAE; TRIAXIAL ACCELEROMETER;

WEARABLE TECHNOLOGY;

ACCELEROMETER; ACCURACY; ADULT; ARTICLE; CHANGING DIRECTION; CLASSIFICATION; CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; CLASSIFIER; CONTROLLED STUDY; COUNTER MOVEMENT JUMPING; DATA EXTRACTION; GENERAL DEVICE; GYROSCOPE SENSOR; HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; JOGGING; JUMPING FOR DISTANCE; LOGISTIC MODEL TREE ALGORITHM; MALE; NORMAL HUMAN; PARAMETERS; PHYSICAL ACTIVITY; PRIORITY JOURNAL; PROCESSING TIME; RANDOM FOREST ALGORITHM; RUNNING; SENSOR; SIMULATION; STATIONARY ACTIVITY; SUPPORT VECTOR MACHINE; TACKLING ACTIVITY; TEAM SPORT ACTIVITY CLASSIFICATION; TIME SERIES ANALYSIS; WALKING; WEARABLE TRACKING DEVICE; YOUNG ADULT; ACCELEROMETRY; ALGORITHM; AMBULATORY MONITORING; DEVICES; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; SPORT;

ACCELEROMETRY; ADULT; ALGORITHMS; HUMANS; MALE; MONITORING, AMBULATORY; MOVEMENT; SPORTS; SUPPORT VECTOR MACHINE; YOUNG ADULT;

EID: 84952863604     PISSN: 00219290     EISSN: 18732380     Source Type: Journal    
DOI: 10.1016/j.jbiomech.2015.09.015     Document Type: Article

References (46)

1. Aminian K., Najafi B. Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications. Comput. Animat. Virtual Worlds 2004, 15:79-94.
2. Aughey R.J., Falloon C. Real-time versus post-game GPS data in team sports. J. Sci. Med. Sport 2010, 13:348-349.
3. Bayat A., Pomplun M., Tran D.A. A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 2014, 34:450-457.
4. Bennett, K.P., Bredensteiner, E.J., 2000. Duality and geometry in SVM classifiers. In: Proceedings of the 17th International Conference on Machine learning. Stanford University, California, USA.
5. Boyd L., Ball K., Aughey R.J. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int. J. Sports Physiol. Perform. 2011, 6:311-321.
6. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
7. Bulling A., Blanke U., Schiele B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 2014, 46:1-33.
8. Carling C., Reilly T., Williams A.M. Performance Assessment for Field Sports 2009, Routledge, New York.
9. Cummins C., Orr R., O'Connor H., West C. Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports Med. 2013, 43:1025-1042.
10. Ermes M., Parkka J., Mantyjarvi J., Korhonen I. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed. 2008, 12:20-26.
11. Gastin P., McLean O., Breed R., Spittle M. Tackle and impact detection in elite Australian football using wearable microsensor technology. J. Sports Sci. 2014, 32:947-953.
12. Gastin P., McLean O., Spittle M., Breed R. Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. J. Sci. Med. Sport. 2013, 16:589-593.
13. Hastie T., Efron B. Least angle regression, lasso and forward stagewise. R package version 1.2 2013.
14. Hastie T., Taylor J., Tibshirani R., Walther G. Forward stagewise regression and the monotone lasso. Electron. J. Stat. 2007, 1:1-29.
15. Hornik K., Buchta C., Zeileis A. Open-source machine learning: R meets Weka. Comput. Stat. 2009, 24:225-232.
16. Kelly D., Coughlan G., Green B., Caulfield B. Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Eng. 2012, 15:81-92.
17. Kunze, K., Bahle, G., Lukowicz, P., Partridge, K., 2010. Can magnetic field sensors replace gyroscopes in wearable sensing applications? In: Proceedings of the 14th IEEE International Symposium on Wearable Computing. Seol, Korea.
18. Landwehr N., Hall M., Frank E. Logistic Model Trees. Mach. Learn. 2005, 59:161-205.
19. Leutheuser H., Schuldhaus D., Eskofier B.M. Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE 2013, 8:e75196.
20. Liaw A., Wiener M. Classification and Regression by randomForest. R News 2002, 2:18-22.
21. Liu H., Motoda H. Feature selection for knowledge discovery and data mining, Springer Science & Business Media 2012, Kluwer Academic Publishers, Norwell, MA.
22. Mallows C.L. More comments on Cp. Technometrics 1995, 37:362-372.
23. Mannini A., Intille S.S., Rosenberger M., Sabatini A.M., Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Med. Sci. Sports Exerc. 2013, 45:2193-2203.
24. McNamara D., Gabbett T., Chapman P., Naughton G., Farhart P. The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int. J. Sports Physiol. Perform. 2015, 10:71-75.
25. Meyer D., Dimitriadou E., Hornik K., Weingessel A., Leisch F. e1071: Misc Functions of the Department of Statistics (e1071) 2014, Comprehensive R Archive Network, TU Wien.
26. Mitchell E., Monaghan D., Connor N. Classification of sporting activities using smartphone accelerometers. Sensors 2013, 13:5317-5337.
27. Morgan S., Williams M.D., Barnes C. Applying decision tree induction for identification of important attributes in one-versus-one player interactions: a hockey exemplar. J. Sports Sci. 2013, 31:1031-1037.
28. Najafi B., Aminian K., Paraschiv-Ionescu A., Loew F., Bula C.J., Robert P. Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 2003, 50:711-723.
29. Nathan R., Spiegel O., Fortmann-Roe S., Harel R., Wikelski M., Getz W.M. Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. J. Exp. Biol. 2012, 215:986-996.
30. Neville, J., Wixted, A., Rowlands, D., James, D., 2010. Accelerometers: an underutilized resource in sports monitoring. In: Proceedings of the 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing. Queensland Univeristy of Technology, Brisbane, Australia.
31. Pärkkä J., Ermes M., Korpipää P., Mäntyjärvi J., Peltola J., Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans. Inf. Technol. Biomed. 2006, 10:119-128.
32. Peacock L., Hewitt A., Rowe D.A., Sutherland R. Stride rate and walking intensity in healthy older adults. J. Aging Phys. Act. 2014, 22:276-283.
33. Preece S.J., Goulermas J., Kenney L.P.J., Howard D. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 2009, 56:871-879.
34. Rabiei M., Gupta R., Cheong Y.P., Sanchez G.A. A novel approach in extracting predictive information from water-oil ratio for enhanced water production mechanism diagnosis. APPEA J. 2012, 50:567-579.
35. Rafiuddin, N., Uzzaman Khan, Y., Farooq, O., 2011. Feature extraction and classification of EEG for automatic seizure detection. In: Proceedings of the International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). Aligarh, Uttar Pradesh, India.
36. Robertson D., Caldwell G., Hamill J., Kamen G., Whittlesey S. Research Methods in Biomechanics 2004, Human Kinetics, South Australia.
37. Robertson S., Back N., Bartlett J.D. Explaining match outcome in elite Australian Rules football using team performance indicators. J. Sports Sci. 2015, 1-8.
38. Singh T.K., Guelfi K.J., Landers G., Dawson B., Bishop D. Reliability of a contact and non-contact simulated team game circuit. J. Sports Sci. Med. 2010, 9:638-642.
39. Srivastava S., Khatri C.G. An introduction to Multivariate Statistics 1979, North-Holland, New York.
40. Sugimoto A., Hara Y., Findley T.W., Yoncmoto K. A useful method for measuring daily physical activity by a three-direction monitor. Scandanavian J. Rehabil. Med. 1997, 29:37-42.
41. Trost S.G., Wong W.K., Pfeiffer K.A., Zheng Y. Artificial neural networks to predict activity type and energy expenditure in youth. Med. Sci. Sports Exerc. 2012, 44:1801-1809.
42. Walpole R.E., Myers R.H., Myers S.L., Ye K. Probability and Statistics for Engineers and Scientists 1993, Macmillan, New York.
43. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, Sydney.
44. Wundersitz D., Gastin P., Richter C., Robertson S., Netto K. Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running. Eur. J. Sport Sci. 2015, 15:382-390.
45. Wundersitz D., Gastin P., Robertson S., Davey P., Netto K. Validation of a Trunk-mounted Accelerometer to Measure Peak Impacts during Team Sport Movements. Int. J. Sports Med. 2015, 36:742-746.
46. Zaki M.J., Meira W. Data Mining and Analysis: Fundamental Concepts and Algorithms 2014, Cambridge University Press, New York.