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An IMU-based mobile system for golf putt analysis

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Abstract

A mobile system for real-time golf putt analysis and augmented feedback is of high interest for technique training and research. Recently, instrumented golf clubs comprising inertial measurement units were introduced as a suitable modality for mobile putt analysis. The high level of sensor integration and its mobile nature enable the unobtrusive and mobile collection of a high amount of data. We developed such a mobile analysis system with feedback capabilities using off-the-shelf components with a removable sensor. The main features are an automatic putt detection with machine learning methods and the real-time parameter calculation in the club coordinate system. In a validation study, the system detected more than 83 % of the putts for 8 out of 11 subjects while maintaining a false-positive rate of 2.4 %. Thus, it is a suitable tool to analyze putting strokes in real time and enables feedback intervention applications. As an application example for research, the collected kinematic data of eight players (1,946 putts) were used to analyze training progress. Compared to the common analysis of expert and novice differences, the presented results provide a first insight in the motor learning path of inexperienced golfers. In principle, the presented system can be used to realize mobile data analysis systems for various sports disciplines beyond golf putting. It furthermore provides an innovative tool to analyze motor learning processes in more detail.

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References

  1. Baca A, Dabnichki P, Heller M, Kornfeind P (2009) Ubiquitous computing in sports: a review and analysis. J Sports Sci 27(12):1335–1346

    Article  Google Scholar 

  2. Hanson MA, Powell HC, Barth AT, Riggenberg K (2009) Body area sensor networks: challenges and opportunities. Computer 42(1):58–65

    Article  Google Scholar 

  3. Pelz D, Frank JA (2000) Dave Pelz’s putting Bible. Doubleday, New York

    Google Scholar 

  4. Karlsen J (2010) Performance in golf putting. Dissertation, Norwegian School of Sport Sciences

  5. McLaughlin P, Best R (2013) Taxonomy of golf putting: do different golf putting techniques exist? J Sports Sci 31(10):1038–1044

    Article  Google Scholar 

  6. Marquardt C (2007) The SAM PuttLab: concept and PGA tour data. Int J Sports Sci Coach 2:101–120

    Article  Google Scholar 

  7. Delay D, Nougier V, Orliaguet J-P, Coello Y (1997) Movement control in golf putting. Hum Mov Sci 16(5):597–619

    Article  Google Scholar 

  8. Sim M, Kim J-U (2010) Differences between experts and novices in kinematics and accuracy of golf putting. Hum Mov Sci 29(6):932–946

    Article  Google Scholar 

  9. Coello Y, Delay D, Nougier V, Orliaguet JP (2000) Temporal control of impact movement: the time from departure control hypothesis in golf putting. Inte J Sport Psychol 31(1):24–46

    Google Scholar 

  10. Paradisis G, Rees J (2000) Kinematic analysis of golf putting for expert and novice golfers. In: Proceedings of the international symposium on biomechanics in sports (ISBS 2000), pp 325–328, HongKong

  11. MacKenzie SJ, Evans DB (2010) Validity and reliability of a new method for measuring putting stroke kinematics using the TOMI system. J Sports Sci 28(8):891–899

    Article  Google Scholar 

  12. King K, Yoon SW, Perkins NC, Najafi K (2008) Wireless MEMS inertial sensor system for golf swing dynamics. Sens Actuators A: Phys 141(2):619–630

    Article  Google Scholar 

  13. Burchfield R, Venkatesan S (2010) A Framework for golf training using low-cost inertial sensors. In: Proceedings of the international conference on body sensor networks (BSN 2010), pp 267–272, Singapore

  14. Kooyman DJ, James DA, Rowlands DD (2013) A feedback system for the motor learning of skills in golf. Procedia Eng 60(1):226–231

    Article  Google Scholar 

  15. Burns A, Green BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V (2010) Shimmer™—a wireless sensor platform for noninvasive niomedical research. IEEE Sens J 10(9):1527–1534

    Article  Google Scholar 

  16. Ferraris F, Grimaldi U, Parvis M (1995) Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers. Sens Mater 7(5):311–330

    Google Scholar 

  17. Kuipers JB (2002) Quaternions and rotation sequences. Princeton University Press, Princeton

    Google Scholar 

  18. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  19. Dietterich TG (2002) Machine learning for sequential data: a review. In: Structural, syntactic, and statistical pattern recognition, vol 2396 of Lecture notes in computer science, Springer-Verlag, pp 15–30

  20. Dadashi F, Arami A, Crettenand F, Millet GP, Komar J, Seifert L, Aminian K (2013) A hidden Markov model of the breaststroke swimming temporal phases using wearable inertial measurement units. In: Proceedings of the international conference on body sensor networks (BSN 2013), page no pagination

  21. Polikar R (2007) Bootstrap-inspired techniques in computational intelligence: ensemble of classifiers, incremental learning, data fusion and missing features. IEEE Signal Process Mag 24(4):59–72

    Article  Google Scholar 

  22. Witten IH, Frank E, Hall MA (2011) Data mining—practical machine learning tools and techniques, 3rd edn. Morgan Kaufman, Amsterdam, pp 487–494

    Google Scholar 

  23. Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the international conference international conference on machine learning, pp 856–863

  24. Carson HJ, Collins D, Richards J (2014) Intra-individual movement variability during skill transitions: a useful marker? Eur J Sport Sci 14(4):327–336

    Article  Google Scholar 

  25. Preatoni E, Hamill J, Harrison AJ, Hayesn K, Van Emmerik RE, Wilson C, Rodano R (2013) Movement variability and skills monitoring in sports. Sports Biomech 12(2):69–92

    Article  Google Scholar 

  26. Liebermann DG, Katz L, Hughes MD, Bartlett RM, McClements J, Franks IM (2002) Advances in the application of information technology to sport performance. J Sports Sci 20(10):755–769

    Article  Google Scholar 

  27. Wagner K (2006) Sport-specific measuring and information systems for training control. Int J Comput Sci Sports 5(2):90–93

    Google Scholar 

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Acknowledgments

This work was funded by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology and the European Fund for Regional Development. The authors would like to thank Alexander Ruppel for his support in data collection and implementation and all subjects who volunteered in the studies.

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Authors and Affiliations

  1. Digital Sports Group, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstrasse 3, 91058, Erlangen, Germany

    Ulf Jensen & Bjoern M. Eskofier

  2. Institute for Sports Sciences, TU Dortmund University, Dortmund, Germany

    Marcus Schmidt, Markus Hennig & Thomas Jaitner

  3. Adidas Group, Herzogenaurach, Germany

    Frank A. Dassler

Authors
  1. Ulf Jensen
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  2. Marcus Schmidt
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  3. Markus Hennig
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  4. Frank A. Dassler
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  5. Thomas Jaitner
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  6. Bjoern M. Eskofier
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Correspondence to Ulf Jensen.

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Jensen, U., Schmidt, M., Hennig, M. et al. An IMU-based mobile system for golf putt analysis. Sports Eng 18, 123–133 (2015). https://doi.org/10.1007/s12283-015-0171-9

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