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Improvement in Hand Trajectory of Reaching Movements by Error-Augmentation

  • Sharon Israely
  • Gerry Leisman
  • Eli Carmeli
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1070)

Abstract

The purpose of this study was to investigate whether adaptive responses to error-augmentation force fields, would decrease the trajectory errors in hand-reaching movements in multiple directions in healthy individuals. The study was conducted, as a randomized controlled trial, in 41 healthy subjects. The study group trained on a 3D robotic system, applying error-augmenting forces on the hand during the execution of tasks. The control group carried out the same protocol in null-field conditions. A mixed-model ANOVA was implemented to investigate the interaction between groups and time, and changes in outcome measures within groups. The findings were that there was a significant interaction effect for group × time in terms of the magnitude of movement errors across game-sets. The trajectory error of the study group significantly decreased from 0.035 ± 0.013 m at baseline to 0.029 ± 0.011 m at a follow-up, which amounted to a 14.8% improvement. The degree of movement errors were not significantly changed within a game-set. We conclude that practicing hand-reaching movement in multiple random directions, using the error-augmentation technique, decreases the deviation of the hand trajectory from a straight line. However, this type of training prevents the generalizability of adaptation between consecutive reaching movements. Further studies should investigate the feasibility of this training method for rehabilitation of post-stroke individuals.

Keywords

Adaptation Brain model Error-augmentation Force-field Hand reaching Hand trajectory 

Notes

Acknowledgments

We would like to thank Dr. Mario Estevez of the Institute for Neurology and Neurosurgery in Havana, Cuba, for his contributions to statistical and research methodology.

References

  1. Bishop L, Khan M, Martelli D, Quinn L, Stein J, Agrawal S (2017) Exploration of two training paradigms using forced induced weight shifting with the tethered pelvic assist device to reduce asymmetry in individuals after stroke: case reports. Am J Phys Med Rehabil 96:S135–S140CrossRefPubMedGoogle Scholar
  2. Cesqui B, Macrì G, Dario P, Micera S (2008) Characterization of age-related modifications of upper limb motor control strategies in a new dynamic environment. J Neuroeng Rehabil 5:31CrossRefPubMedPubMedCentralGoogle Scholar
  3. Conditt MA, Gandolfo F, Mussa-Ivaldi FA (1997) The motor system does not learn the dynamics of the arm by rote memorization of past experience. J Neurophysiol 78:554–560CrossRefPubMedGoogle Scholar
  4. Doig GS, Simpson F (2005) Randomization and allocation concealment: a practical guide for researchers. J Crit Care 20:187–191CrossRefPubMedGoogle Scholar
  5. Donchin O, Francis JT, Shadmehr R (2003) Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basic functions: theory and experiments in human motor control. J Neurosci 23:9032–9045CrossRefPubMedGoogle Scholar
  6. Emken JL, Reinkensmeyer DJ (2005) Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification. IEEE Trans Neural Syst Rehabil Eng 13:33–39CrossRefPubMedGoogle Scholar
  7. Givon-Mayo R, Simons E, Ohry A, Karpin H, Israely S, Carmeli E (2014) A preliminary investigation of error enhancement of the velocity component in stroke patients’ reaching movements. Int J Ther Rehabil 21:160–168CrossRefGoogle Scholar
  8. Goodbody SJ, Wolpert DM (1998) Temporal and amplitude generalization in motor learning. J Neurophysiol 79:1825–1838CrossRefPubMedGoogle Scholar
  9. Haith AM, Krakauer JW (2013) Model-based and model-free mechanisms of human motor learning. Adv Exp Med Biol 782:1–21CrossRefPubMedPubMedCentralGoogle Scholar
  10. Hanlon RE (1996) Motor learning following unilateral stroke. Arch Phys Med Rehabil 77:811–815CrossRefPubMedGoogle Scholar
  11. Huang FC, Patton JL (2013) Augmented dynamics and motor exploration as training for stroke. IEEE Trans Biomed Eng 60:838–844CrossRefPubMedGoogle Scholar
  12. Huang VS, Haith A, Mazzoni P, Krakauer JW (2011) Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models. Neuron 70:787–801CrossRefPubMedPubMedCentralGoogle Scholar
  13. Israely S, Carmeli E (2016) Error augmentation as a possible technique for improving upper extremity motor performance after a stroke – a systematic review. Top Stroke Rehabil 23:116–125CrossRefPubMedGoogle Scholar
  14. Izawa J, Criscimagna-Hemminger SE, Shadmehr R (2012) Cerebellar contributions to reach adaptation and learning sensory consequences of action. J Neurosci 32:4230–4239CrossRefPubMedPubMedCentralGoogle Scholar
  15. Jonsdottir J, Cattaneo D, Regola A, Crippa A, Recalcati M, Rabuffetti M, Ferrarin M, Casiraghi A (2007) Concepts of motor learning applied to a rehabilitation protocol using biofeedback to improve gait in a chronic stroke patient: an AB system study with multiple gait analyses. Neurorehabil Neural Repair 21:190–194CrossRefPubMedGoogle Scholar
  16. Krakauer JW, Ghez C, Ghilardi MF (2005) Adaptation to visuomotor transformations: consolidation, interference, and forgetting. J Neurosci 25:473–478CrossRefPubMedGoogle Scholar
  17. Krakauer JW, Carmichael ST, Corbett D, Wittenberg GF (2012) Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair 26:923–931CrossRefPubMedPubMedCentralGoogle Scholar
  18. Lai Q, Shea CH, Wulf G, Wright DL (2000) Optimizing generalized motor program and parameter learning. Res Q Exerc Sport 71:10–24CrossRefPubMedGoogle Scholar
  19. Lewek MD, Braun CH, Wutzke C, Giuliani C (2017) The role of movement errors in modifying spatiotemporal gait asymmetry post stroke: a randomized controlled trial. Clin Rehabil 1:269215517723056.  https://doi.org/10.1177/0269215517723056 CrossRefGoogle Scholar
  20. Molier BI, Prange GB, Krabben T, Stienen A, van der Kooij H, Buurke JH, Jannink MJ, Hermens HJ (2011) Effect of position feedback during task-oriented upper-limb training after stroke: five-case pilot study. J Rehabil Res Dev 48:1109–1118CrossRefPubMedGoogle Scholar
  21. O’Brien K, Crowell CR, Schmiedeler J (2017) Error augmentation feedback for lateral weight shifting. Gait Posture 54:178–182CrossRefPubMedGoogle Scholar
  22. Orban de Xivry JJ, Lefevre P (2015) Formation of model-free motor memories during motor adaptation depends on perturbation schedule. J Neurophysiol 113:2733–2741CrossRefPubMedPubMedCentralGoogle Scholar
  23. Patton JL, Kovic M, Mussa-Ivaldi FA (2006a) Custom-designed haptic training for restoring reaching ability to individuals with poststroke hemiparesis. J Rehabil Res Dev 43:643–656CrossRefPubMedGoogle Scholar
  24. Patton JL, Stoykov ME, Kovic M, Mussa-Ivaldi FA (2006b) Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res 168:368–383CrossRefPubMedGoogle Scholar
  25. Rozario SV, Housman S, Kovic M, Kenyon RV, Patton JL (2009) Therapist-mediated post-stroke rehabilitation using haptic/graphic error augmentation. Conf Proc IEEE Eng Med Biol Soc 2009:1151–1156PubMedGoogle Scholar
  26. Sainburg RL, Ghez C, Kalakanis D (1999) Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms. J Neurophysiol 81:1045–1056CrossRefPubMedGoogle Scholar
  27. Shadmehr R, Brashers-Krug T (1997) Functional stages in the formation of human long-term motor memory. J Neurosci 17:409–419CrossRefPubMedGoogle Scholar
  28. Shadmehr R, Moussavi ZM (2000) Spatial generalization from learning dynamics of reaching movements. J Neurosci 20:7807–7815CrossRefPubMedGoogle Scholar
  29. Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14(5 Pt 2):3208–3224CrossRefPubMedGoogle Scholar
  30. Williams CK, Tremblay L, Carnahan H (2016) It pays to go off-track: practicing with error-augmenting haptic feedback facilitates learning of a curve-tracing task. Front Psychol 7:2010PubMedPubMedCentralGoogle Scholar
  31. Yen SC, Landry JM, Wu M (2014) Augmented multisensory feedback enhances locomotor adaptation in humans with incomplete spinal cord injury. Hum Mov Sci 35:80–93CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Department of Physical TherapyUniversity of HaifaHaifaIsrael
  2. 2.The National Institute for Brain and Rehabilitation Sciences-IsraelNazarethIsrael

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