Experimental Brain Research

, Volume 231, Issue 3, pp 277–291 | Cite as

The effect of haptic guidance and visual feedback on learning a complex tennis task

  • Laura Marchal-Crespo
  • Mark van Raai
  • Georg Rauter
  • Peter Wolf
  • Robert Riener
Research Article

Abstract

While haptic guidance can improve ongoing performance of a motor task, several studies have found that it ultimately impairs motor learning. However, some recent studies suggest that the haptic demonstration of optimal timing, rather than movement magnitude, enhances learning in subjects trained with haptic guidance. Timing of an action plays a crucial role in the proper accomplishment of many motor skills, such as hitting a moving object (discrete timing task) or learning a velocity profile (time-critical tracking task). The aim of the present study is to evaluate which feedback conditions—visual or haptic guidance—optimize learning of the discrete and continuous elements of a timing task. The experiment consisted in performing a fast tennis forehand stroke in a virtual environment. A tendon-based parallel robot connected to the end of a racket was used to apply haptic guidance during training. In two different experiments, we evaluated which feedback condition was more adequate for learning: (1) a time-dependent discrete task—learning to start a tennis stroke and (2) a tracking task—learning to follow a velocity profile. The effect that the task difficulty and subject’s initial skill level have on the selection of the optimal training condition was further evaluated. Results showed that the training condition that maximizes learning of the discrete time-dependent motor task depends on the subjects’ initial skill level. Haptic guidance was especially suitable for less-skilled subjects and in especially difficult discrete tasks, while visual feedback seems to benefit more skilled subjects. Additionally, haptic guidance seemed to promote learning in a time-critical tracking task, while visual feedback tended to deteriorate the performance independently of the task difficulty and subjects’ initial skill level. Haptic guidance outperformed visual feedback, although additional studies are needed to further analyze the effect of other types of feedback visualization on motor learning of time-critical tasks.

Keywords

Haptic guidance Visual feedback Motor learning Timing task 

Notes

Acknowledgments

The authors gratefully acknowledge the contributions of Dario Wyss, Andreas Neidhart, and Michael Herold-Nadig. The authors thank the Statistical Consulting service at ETH, Zurich, for their assistance in the statistical analysis. Laura Marchal-Crespo holds a Marie Curie International Income fellowship PIIF-GA-2010-272289.

References

  1. Aoyagi D, Ichinose WE, Harkema SJ, Reinkensmeyer DJ, Bobrow JE (2007) A robot and control algorithm that can synchronously assist in naturalistic motion during body-weight-supported gait training following neurologic injury. Neural Syst Rehabil Eng IEEE Trans 15(3):387–400. doi: 10.1109/TNSRE.2007.903922 CrossRefGoogle Scholar
  2. Bluteau J, Coquillart S, Payan Y, Gentaz E (2008) Haptic guidance improves the visuo-manual tracking of trajectories. PLoS ONE 3(3):e1775. doi: 10.1371/journal.pone.0001775 PubMedCrossRefGoogle Scholar
  3. Domingo A, Ferris D (2010) The effects of error augmentation on learning to walk on a narrow balance beam. Exp Brain Res 206(4):359–370. doi: 10.1007/s00221-010-2409-x PubMedCrossRefGoogle Scholar
  4. Duschau-Wicke A, von Zitzewitz J, Caprez A, Lunenburger L, Riener R (2010) Path control: a method for patient-cooperative robot-aided gait rehabilitation. Neural Syst Rehabil Eng IEEE Trans 18(1):38–48. doi: 10.1109/TNSRE.2009.2033061 CrossRefGoogle Scholar
  5. Emken J, Benitez R, Reinkensmeyer D (2007) Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed. J NeuroEng Rehabil 4(1):8PubMedCrossRefGoogle Scholar
  6. Feygin D, Keehner M, Tendick F (2002) Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill. In: Haptic interfaces for virtual environment and teleoperator systems, 2002. HAPTICS 2002. Proceedings. 10th symposium on 2002, pp 40–47. doi: 10.1109/HAPTIC.2002.998939
  7. Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7):1688–1703PubMedGoogle Scholar
  8. Guadagnoli MA, Lee TD (2004) Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav 36(2):212–224. doi: 10.3200/JMBR.36.2.212-224 PubMedCrossRefGoogle Scholar
  9. Hua-wei L, Tao M, Meng M (2006) Design and implementation of a fencing training robot. In: Intelligent robots and systems, 2006 IEEE/RSJ international conference on 9–15 Oct 2006, pp 3624–3627. doi: 10.1109/IROS.2006.281716
  10. Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: Robotics and automation. Proceedings. 1985 IEEE international conference on Mar 1985, pp 500–505. doi: 10.1109/ROBOT.1985.1087247
  11. Lee M, Moseley A, Refshauge K (1990) Effect of feedback on learning a vertebral joint mobilization skill. Phys Ther 70(2):97–102PubMedGoogle Scholar
  12. Marchal-Crespo L, Reinkensmeyer DJ (2008) Effect of robotic guidance on motor learning of a timing task. In: Biomedical robotics and biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS international conference on 19–22 Oct 2008, pp 199–204. doi: 10.1109/BIOROB.2008.4762796
  13. Marchal-Crespo L, Reinkensmeyer D (2009) Review of control strategies for robotic movement training after neurologic injury. J NeuroEng Rehabil 6(1):20PubMedCrossRefGoogle Scholar
  14. Marchal-Crespo L, Furumasu J, Reinkensmeyer D (2010a) A robotic wheelchair trainer: design overview and a feasibility study. J NeuroEng Rehabil 7(1):40PubMedCrossRefGoogle Scholar
  15. Marchal-Crespo L, McHughen S, Cramer S, Reinkensmeyer D (2010b) The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task. Exp Brain Res 201(2):209–220. doi: 10.1007/s00221-009-2026-8 PubMedCrossRefGoogle Scholar
  16. Marchal-Crespo L, Rauter G, Wyss D, von Zitzewitz J, Riener R (2012) Synthesis and control of an assistive robotic tennis trainer. In: Biomedical robotics and biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS international conference on 24–27 June 2012, pp 355–360. doi: 10.1109/BioRob.2012.6290262
  17. Milot M-H, Marchal-Crespo L, Green C, Cramer S, Reinkensmeyer D (2010) Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals. Exp Brain Res 201(2):119–131. doi: 10.1007/s00221-009-2014-z PubMedCrossRefGoogle Scholar
  18. Morizono T, Kurahashi K, Kawamura S (1997) Realization of a virtual sports training system with parallel wire mechanism. In: Robotics and automation, vol 3024, 1997. Proceedings, 1997 IEEE international conference on 20–25 Apr 1997, pp 3025–3030. doi: 10.1109/ROBOT.1997.606747
  19. Morris D, Hong T, Barbagli F, Chang T, Salisbury K (2007) Haptic feedback enhances force skill learning. In: EuroHaptics conference, 2007 and symposium on haptic interfaces for virtual Environment and Teleoperator Systems. World haptics 2007. second joint, 22–24 Mar 2007, pp 21–26. doi: 10.1109/WHC.2007.65
  20. Olsson H, Åström KJ, Canudas de Wit C, Gäfvert M, Lischinsky P (1998) Friction models and friction compensation. Eur J Control 4:176–195CrossRefGoogle Scholar
  21. Proteau L (2005) Visual afferent information dominates other sources of afferent information during mixed practice of a video-aiming task. Exp Brain Res 161(4):441–456PubMedCrossRefGoogle Scholar
  22. Rauter G, von Zitzewitz J, Duschau-Wicke A, Vallery H, Riener R (2010) A tendon-based parallel robot applied to motor learning in sports. In: Biomedical robotics and biomechatronics (BioRob), 2010 3rd IEEE RAS and EMBS international conference on IEEE, pp 82–87Google Scholar
  23. Rauter G, Sigrist R, Marchal-Crespo L, Vallery H, Riener R, Wolf P (2011) Assistance or challenge? Filling a gap in user-cooperative control. In: Intelligent robots and systems (IROS), 2011 IEEE/RSJ international conference on 25–30 Sept 2011, pp 3068–3073. doi: 10.1109/IROS.2011.6094832
  24. Reinkensmeyer DJ, Akoner OM, Ferris DP, Gordon KE (2009) Slacking by the human motor system: computational models and implications for robotic orthoses. In: Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE, pp 2129–2132Google Scholar
  25. Salmoni A, Schmidt R, Walter C (1984) Knowledge of results and motor learning: a review and critical reappraisal. Psychol Bull 95(3):355–386PubMedCrossRefGoogle Scholar
  26. Schmidt RA, Walter CB (1984) Knowledge of results and motor learning: a review and critical reappraisal. Psychol Bull 95(3):355–386PubMedCrossRefGoogle Scholar
  27. Schmidt RA, Wrisberg CA (2008) Motor learning and performance: a situation-based learning approach. Human Kinetics Publishers, CambridgeGoogle Scholar
  28. Shea CH, Wulf G (1999) Enhancing motor learning through external-focus instructions and feedback. Hum Mov Sci 18(4):553–571CrossRefGoogle Scholar
  29. Sigrist R, Rauter G, Riener R, Wolf P (2013) Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev 20(1):21–53. doi: 10.3758/s13423-012-0333-8 PubMedCrossRefGoogle Scholar
  30. Todorov E, Shadmehr R, Bizzi E (1997) Augmented feedback presented in a virtual environment accelerates learning of a difficult motor task. J Mot Behav 29(2):147–158PubMedCrossRefGoogle Scholar
  31. von Zitzewitz J, Rauter G, Steiner R, Brunschweiler A, Riener R (2009) A versatile wire robot concept as a haptic interface for sport simulation. In: Robotics and automation, 2009. ICRA’09. IEEE international conference on IEEE, pp 313–318Google Scholar
  32. von Zitzewitz J, Morger A, Rauter G, Marchal-Crespo L, Crivelli F, Wyss D, Bruckmann T, Riener R (2013) A reconfigurable, tendon-based haptic interface for research into human-environment interactions. Robotica 31(03):441–453. doi: 10.1017/S026357471200046X CrossRefGoogle Scholar
  33. Wulf G, Shea CH (2002) Principles derived from the study of simple skills do not generalize to complex skill learning. Psychon Bull Rev 9(2):185–211PubMedCrossRefGoogle Scholar
  34. Wulf G, Shea CH, Whitacre CA (1998) Physical-guidance benefits in learning a complex motor skill. J Mot Behav 30(4):367–380PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Laura Marchal-Crespo
    • 1
    • 2
  • Mark van Raai
    • 1
    • 2
  • Georg Rauter
    • 1
    • 2
  • Peter Wolf
    • 1
    • 2
  • Robert Riener
    • 1
    • 2
  1. 1.Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS)ETH ZurichZurichSwitzerland
  2. 2.Medical Faculty, Balgrist University HospitalUniversity of ZurichZurichSwitzerland

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