Artificial Intelligence Review

, Volume 27, Issue 1, pp 57–70 | Cite as

AI and computer-based methods in performance evaluation of sporting feats: an overview

Article

Abstract

Performance evaluation is a complex process, usually involving the analyses of large amounts of possibly subjective information. The complexity increases when the performances of more than one athlete are being evaluated. For example a coach in charge of twenty divers should be able to remember the strengths and weaknesses of each athlete. Given these difficulties, it is therefore not surprising that a number of computer-based systems have been developed to speed-up and improve the quality of performance evaluation. Most of these systems are visually based such that individuals working on performance analysis first record the motion in question by electronic means and then input these images into a computer for quantification and subsequent analysis. There seems to be enormous potential for AI (i.e. Artificial Intelligence) technologies to make a significant contribution in the analysis phase. Indeed AI technologies have been applied to performance evaluation in recent years, though their applicability has been limited for a variety of reasons. The main factor has been a lack of characterisation of the domain of performance evaluation. This paper reviews selected research and applications of computational models and AI technologies in particular in performance evaluation of sporting feats for individual based events.

Keywords

Computational models AI technologies Performance evaluation of sporting feats Critiquing of sporting feats 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abas J (1988) Computers in health and fitness. Chartwell Bratt, LondonGoogle Scholar
  2. Alford J (1990) The sprint master: an interactive computer program. New Stud Athl 2: 31–32Google Scholar
  3. Ariel GB (1996) Biomechanical analysis of sports and training in local and global microcosm: a biomechanical step onto the Internet. In: Proceedings of symposium on biomechanics in sports, vol 14, pp 107–110Google Scholar
  4. Austin H (1974) A computational view of the skill of juggling. Artificial Intelligence Memo Number 330, MIT AI LabGoogle Scholar
  5. Burton RR (1982) Diagnosing bugs in a simple procedural skill. In: Sleeman DH, Brown JS (eds) Intelligent tutoring systems, pp 157–183Google Scholar
  6. Byrne N (1995) Moving sports analysis. Image Process 7: 20–22Google Scholar
  7. Clancey WJ (1987) The role of qualitative models in instruction. In: Self J(eds) Artificial intelligence and human learning: intelligent computer-aided instruction. Chapman and Hall, London, pp 49–68Google Scholar
  8. Durkin J (1994) Expert systems: design and development. Prentice HallGoogle Scholar
  9. Fairs JR (1987) The coaching process: the essence of coaching. Sports Coach 13: 16–22Google Scholar
  10. Fernandes O, Anes E, Abrantes JMCS (1996) Biosist—two dimensional kinematic performance analysis. In: Proceedings of symposium on biomechanics in sports, vol 14, pp 189–192Google Scholar
  11. Fletcher MJ (1991) A modular system for video based motion analysis. PhD Thesis, University of Reading, UKGoogle Scholar
  12. Freeman WH (1990) The ethical implications of expert systems in sport training: the need for ethical limits to striving. New Stud Athl 2: 16–21Google Scholar
  13. Frischholz RW, Spinnler KP (1993) A class of algorithms for real-time subpixel registration. In: Proceedings Europto series, vol 1989, Munchen, GermanyGoogle Scholar
  14. Gambetta V (1990) The computer and the coach. New Stud Athl 2: 7–15Google Scholar
  15. Glencross DJ (1992) Human skill and motor learning: a critical review. Sports Sci Rev 1: 65–78Google Scholar
  16. Hart A (1989) Machine induction as a form of knowledge acquisition in knowledge engineering. In: Forsyth~ R(eds) Machine learning principles and techniques. Chapman and Hall, London, pp 23–38Google Scholar
  17. Hay JG (1991) Reaction to performance feedback: advances in biomechanics. In: Enhancing human performance in sport: new concepts and developments. Human Kinematics Publishers, pp 33–37Google Scholar
  18. Hay JG, Reid JG (1988) Anatomy, mechanics and human motion. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  19. Higgins JR (1977) Human movement: an integrated approach. The C.V. Mosby CompanyGoogle Scholar
  20. Iwasaki Y (1980) Qualitative physics. In: Barr A, Cohen PR, Feigenbaum PR (eds) The handbook of Artificial Intelligence IV, Addison-Wesley Publishing Company Inc, pp 323–413Google Scholar
  21. Jackson P (1990) Introduction to expert systems. Addison-Wesley Publishing LtdGoogle Scholar
  22. Kirtley C, Philips R (1996). Motion toolbox: an interactive multimedia package for studying human movement. In: Proceedings of 3rd interactive multimedia symposium. Perth, Western Australia, pp 197–201Google Scholar
  23. Kreighbaum E, Barthels KM (1990) Biomechanics: a qualitative approach for studying human movement. Macmillan Publishing CompanyGoogle Scholar
  24. Lapham AC, Bartlett RM (1995) The use of artificial intelligence in the analysis of sports performance: a review of applications in human gait analysis and future directions for sports biomechanics. J Sports Sci 13: 229–237CrossRefGoogle Scholar
  25. Magill RA (1992) Practice schedule considerations for enhancing human performance in sport. Quest 25: 38–50Google Scholar
  26. Martin WN, Aggarwal JK (1978) Survey dynamic scene analysis. Comput Graph Image Process 7: 356–374CrossRefGoogle Scholar
  27. Masson MEJ (1990) Cognitive theories of skill acquisition. Human Mov Sci 9: 221–239CrossRefGoogle Scholar
  28. Miller DI (1975) Computer simulation of human motion. In: Grieve DW, Miller D, Mitchelson D, Paul J, Smith AJ (eds) Techniques for the analysis of human movement, pp 69–105Google Scholar
  29. Nwana HS, Bench-Capon TJM, Paton RC, Shave MJR (1994) Domain-driven knowledge modelling for knowledge acquisition. Knowl Acquis 6: 243–270CrossRefGoogle Scholar
  30. Owen R (1989) Image analysis. IEE Rev 77–79Google Scholar
  31. Pazos A, Rivas A, Barral R (1996) Recognition of human movement patterns. In: Proceedings of symposium on biomechanics in sports, vol 14, pp 151–158Google Scholar
  32. Sands WA (1992) AI and athletes. PC AI 6: 52–54Google Scholar
  33. Sands WA (1994) Kinesiological motion expert system. Comput Meth Programs Biomed 45: 261–263CrossRefGoogle Scholar
  34. Turner R, Newton J (1992) An automated system for 4D motion analysis. In: Proceedings of biomechanics in gymnastics. Cologne, Germany, pp 210–217Google Scholar
  35. Vickers JN, Kingston GE (1987) Modelling the master coach: building an expert system for coaching. In: Proceedings of the international conference on computer assisted learning in post-secondary education, pp 207–212Google Scholar
  36. Wallingford RR (1974) Evaluation of an application of a computer retrieval system for exercise physiology. Unpublished PhD DissertationGoogle Scholar
  37. Williams J (1994) The use and capture of images for computer-based learning. University of Bristol, UKGoogle Scholar
  38. Wooten WL, Hodgins JK (1996) Animation of human diving. Comput Graph Forum 15: 3–13CrossRefGoogle Scholar
  39. Yeadon MR (1990) Simulation of aerial movement-I, II, III. J Biomech 23: 59–83CrossRefGoogle Scholar
  40. Yeadon MR, Challis JH (1994) The future of performance-related biomechanics research. J Sports Sci 12: 3–32CrossRefGoogle Scholar
  41. Yeadon MR, Atha J, Hales FD (1990) The simulation of aerial movement—IV: a computer simulation. J Biomech 23: 85–89CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.BT PLC, Office of Group CTOIntelligent Systems Research CentreMarthlesham HeathUK

Personalised recommendations