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What to expect in the next 4 years of Computer Science in Sport

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Opening Keynote Address at IACSS09

Opening Keynote Address at IACSS09

Published in: Sports, Technology, Education

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  • In the 1950ies US mainframe computer : Thunderstorm, Typhoon, … First Computer on European continent entirely working with transistors.
  • Perl: Plenary talk, 5th Int. Symposium, Hvar
  • Perl: Plenary talk, 5th Int. Symposium, Hvar
  • Perl: Plenary talk, 5th Int. Symposium, Hvar
  • Perl: Plenary talk, 5th Int. Symposium, Hvar
  • Grün: Model based data analysis
  • Moore's Law describes a long-term trend in the history of computing hardware . Since the invention of the integrated circuit in 1958, the number of transistors that can be placed inexpensively on an integrated circuit has increased exponentially , doubling approximately every two years. [1] The trend has continued for more than half a century and is not expected to stop until 2015 or even later. [2]
  • Informatics, because aspects of social science are considered, also. A project proposal on this topic has just been worked out.
  • Applicability in biomechanics currently investigated.
  • Anwendbarkeit in der Biomechanik sollte sich in den nächsten 4 Jahren entscheiden
  • Games: Some developments in collabarative games – no breakthrough. Ubiquitous Fitnes support: e.g. advice for mountain bikers or runners, which direction to go (GPS + heartrate) – e.g. suggestion th run/drive an easier route.
  • e.g. Instruction to change gear in order to increase cadence.
  • Standardisation – still a challenge. Computer scientists should get aware of the benefit they might get from sport scientists. Benefit for computer scientists: computer games, animation, robotics
  • Transcript

    • 1. What to expect in the next 4 years of Computer Science in Sport Arnold Baca Department of Biomechanics, Kinesiology and Applied Computer Science ZSU University of Vienna
    • 2. Prof. Heinz Zemanek * 1. 1. 1920 Mailüfterl , 1955 (From: mailuefterl.at/images/mailu/control.jpg )
    • 3.
      • Introduction
      • Current Trends (This conference)
      • Trends in Computer Science
      • Megatrends – Main developments
        • Ubiquitous Computing
        • Data Analysis Methods
      • Summary/Expectations
      Survey
    • 4. Introduction
      • Jürgen Perl, 2006 :
      • “ Future development of rapidly changing fields like CSS is difficult to predict“
      • 2 aspects:  Amount of available data increases permanently – how to get the useful information?
      •  World wide communication needs improved IT
      • Computer Science in Sport: An Overview of Present Fields and Future Applications (Int J Comp Sci Sport, Spec. Ed., 2006)
    • 5. Introduction
      • Josef Wiemeyer, 2006 :
      • “ The quality and quantity of research in computer science in sport grow continuously. One future task is to keep pace with the development of computer technology and computer science“.
      • “ Aktuelle Trends in der Sportinformatik“ [“Current Trends in Computer Science in Sport“] (Leipziger Sportwissenschaftliche Beiträge, 47, pp. 19-38).
    • 6. Introduction
      • Daniel Link / Martin Lames, 2008 :
      • Forecasts until 2020:
      • (1) Positioning systems and lightweight sensors can be used for the capturing of total physiological and positional data. This information will be provided to trainers, athletes and mass media in real time.
      • (2) Based on the enhancements in the field of artificial intelligence, tactical behavior in sport games can be analyzed automatically.
      • (3) Virtual environments can simulate many different sports close to reality. This allows a training of perception, cognition and decision in sport specific situations.
      • (4) New display technologies will be integrated in sport clothes (e.g. sun glasses) and provide athletes with information during training and competition.
      • (5) Sport uses smart clothes including sensory (measuring and transmitting of data) and actuator (adaptation of material characteristics) features for diagnosis, prevention and performance improvement .
      • “ Matrix Reloaded – Struktur und Gegenstand der Sportinformatik“ [“Matrix Reloaded – Structure and Subject of Computer Science in Sport“ (Schriften der dvs, 189, pp. 11-32).
    • 7. Introduction
      • Daniel Link / Martin Lames, 2008 :
      • Forecasts until 2020:
      • (6) Biomechanical models can simulate human movements perfectly, without using motion capturing or key frames. This allows the animation of virtual characters (e.g. for computer games), which look and move exactly like humans. These models can also be used to make the motor behavior of humanoid robots more manlike.
      • (7) Simulation has become an important research tool for natural scientific disciplines. The simulation of training processes (e.g. performance adaptation) can replace the real experiment in many cases.
      • (8) The increased computational power allows simulating the vibration and flow behavior of sport devices and athletes more realistic than today (e.g. flow resistance of swimmers).
      • (9) The relevance of sport clubs as a local organizer of training sessions decreases. Group building via social networks on the Internet gets more important for everyday sportspersons.
      • (10) In-class lectures at universities will lose their importance. Almost 50   % of theoretical courses and seminars in sport science will be replaced by online equivalents.
      • “ Matrix Reloaded – Struktur und Gegenstand der Sportinformatik“ [“Matrix Reloaded – Structure and Subject of Computer Science in Sport“ (Schriften der dvs, 189, pp. 11-32).
    • 8.
      • This Conference
      Current Trends
    • 9. Current Trends – This Conference (40 Presentations) Sensors/Wireless Technologies/Tracking 10 Modelling 5 Game/Performance Analysis 5 Data Analysis (Scoring/Ranking) 3 13 To be considered with caution!
    • 10. Trends in Computer Science
      • Moore‘s Law
      • … not expected to stop until 2015
      • (M. Kanellos, 2005)
      Source: Intel
    • 11.
      • Miniaturisation
      Trends in Computer Science
    • 12.
      • Wireless Technologies
      • Networks
      • Mobile Ad Hoc Networks
      •  Mobile devices connected to a network by wireless links (self-configuration)
      Trends in Computer Science
    • 13.
      • Open Source
      •  Hardware
      •  Software
      • Cloud Computing
      Trends in Computer Science
    • 14.
      • Social Networks
      Trends in Computer Science / Informatics Example: InteractivE-Training for Elderly People
    • 15.
      • Pervasive/Ubiquitous Computing
      • Data Processing/Analysis
      Megatrends
    • 16.
      • Pervasive/Ubiquitous Computing
      • Data Processing/Analysis
      Megatrends
    • 17.  
    • 18. Pervasive/ubiquitous computing in sports
      • 2 categories:
      • (1) Technological developments in miniaturisation of the devices and increasing their ability to transmit data at considerable distances and
      • (2) Development of algorithms for fast and/or accurate data processing in (speedy) feedback provision that represents a firm step in the direction towards intelligent systems.
      A. Baca, P. Dabnichki, M. Heller, P. Kornfeind, Journal of Sports Sciences, in press
    • 19. Feedback Systems
    • 20. Feedback Systems A. Baca, P. Dabnichki, M. Heller, P. Kornfeind, Journal of Sports Sciences, in press
    • 21. Rowing oarlock foot stretcher d y n a m o m e t e r s outrigger
    • 22. PDA-System PDA + NI DAQ Card Wireless LAN
      • Sensors in boat (e.g. oarlock dynamometer, oar angle,...)
      • Data acquisition using PDA (+ data acquisition card)
      • Online-transmission via WLAN
      • Online-Feedback
      Rowing
    • 23. Table Tennis
    • 24. Table Tennis
    • 25. Target Athlete Digital Video Camera 50m Biathlon 5m 1m ~1.6m above ground
    • 26. Biathlon
    • 27.
      • ->
      Training in natural, complex and ecological valid situations
    • 28. Tracking Systems
    • 29. Source: http://www.zonefivesoftware.com/SportTracks / GPS based systems
    • 30. Player tracking Radio- & microwave based systems Transponder
    • 31. Beetz et al. Proc. 5th Int. Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006. Player tracking
    • 32. Kinematics Bandouch et al.. Proc. 5th Int. Conf. on Articulated Motion and Deformable Objects (AMDO), 2008. Courtesy of M. Beetz, TU Munich
    • 33.
      • Co-operation Computer Science – Sport Science (Human body models, joint centres, …)
      ->
    • 34. Leisure and Entertainment
    • 35. Leisure and Entertainment Games, e.g. Mountainbike, Marathon Ubiquitous-fitness support Broadcast
    • 36. Example: RFID-chips in running
    • 37. Example: Training in virtual environments B. Bideau et al. (2003) Presence-Teleoperators and Virtual Environments, 12 (4), 2003.
    • 38. Example: Wearable Computing A. Baca, P. Dabnichki, M. Heller, P. Kornfeind, Journal of Sports Sciences , in press Electronic circuits are woven or printed on textiles and allow measuring a variety of physiological parameters directly.
    • 39. Example: Wearable Computing 13 th Int. Symposium on Wearable Computers, September 2009, Linz, Austria: Highest potential of “Wearable Computing“‚ in the fields of medicine, health and sport. Gerhard Tröster , Head, Wearable Computing Group and Laboratory, ETH Zurich Application: Monitoring of snowboarders using sensors on the board and the body (clothes) to identify riding mistakes and to improve the style.
    • 40.
      • Pervasive/Ubiquitous Computing
      • Data Processing/Analysis
      Megatrends
    • 41.
      • Automatic position oriented game protocol (Grunz, Memmert, Perl, 2009)
      Player + team analysis
    • 42.
      • Basketball free shots (Grunz, Memmert, Perl, 2009) – From PhD-thesis A. Schmidt
      Process models
    • 43.
      • Biathlon Shooting (Baca, Perl, Kornfeind, Böcskör, 2009)
      Process models A 1 2 C 1 2 D 1 2 Color profile: sorted by total muzzle velocity
    • 44. Process models Similarity Analysis: 3 athletes (before/after load, 16 shots) good stability less stable unstable very unstable A 1 A 2 C 1 C 2 D 1 D 2 before physical load after physical load
    • 45. Classifying sports and physical activity
      • Positions, velocities and accelerations
      • Heart rate, heat, force (touch), respiration…
      • Wearable devices
      Weber et al. International Journal of Computer Science in Sport 6 (2), 2007
    • 46.
      • Pervasive/Ubiquitous Computing
      • Data Processing/Analysis
      Megatrends
    • 47. Intelligent Systems
      • Revival of Expert Systems?
      > 4 years
    • 48. Remote Coaching System Overview Expert system
    • 49. Remote Coaching – Hardware SPI Analog IN E.g. temperature, air pressure, potentiometer, … Digital I/O E.g. on-off switch, trigger, … ANT Modul  Controller Bridge Amplifier Strain gages Force sensors Accelerometer (strain gage based) Memory card „ Managed Networks“
    • 50. Sports medicine specialist (physiological data) Biomechanist (dynamometric data) Athlete (e.g. report; + full access) Remote Coaching – Software Expert system Lotus Expeditor DB2-Database Device Manager Lotus Expeditor CLIENT Lotus Expeditor CLIENT
    • 51. Remote Coaching Example: Rowing
    • 52. Remote Coaching Example: Mountain biking Example: Mountain biking
    • 53. Summary
      • What will be achieved within the next 4 years ?
      • Some approaches of Social Networks
      • Markerless methods in motion analysis: Decision basis on applicability in Biomechanics
      • Use of Ubiquitous Computing Technologies
          • Analysis systems
          • Coaching and training systems
          • Decision systems
          • Leisure and entertainment systems
      • (Model based) data analysis : Some progression into a forward-looking future
    • 54.
      • Deepened integration of computer scientists
      • Specific CSS education for computer scientists
      • Standardisation/sustainability (e.g. ANT ™ )
      • Ease in application (without additional manpower)
      Expectations
    • 55. Thank you for your attention!