Skip to main content
SpringerLink
Log in

Error Augmentation and the Role of Sensory Feedback

  • Chapter
  • First Online:
Neurorehabilitation Technology

Abstract

Brain injury often results a partial loss of the neural resources communicating to the periphery that controls movements. Consequently, the prior signals may no longer be appropriate for getting the muscles to do what is needed – a new pattern needs to be learned that appropriately uses the residual resources. Such learning may not be too different from the learning of skills in sports, music performance, surgery, teleoperation, piloting, and child development. Our lab has leveraged what we know about neural adaptation and engineering control theory to develop and test new interactive environments that enhance learning (or relearning). One successful application is the use of robotics and video feedback technology to augment error signals, which tests standing hypotheses about error-mediated neuroplasticity and illustrates an exciting prospect for rehabilitation environments of tomorrow.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hollerbach JM, Flash T. Dynamic interactions between limb segments during planar arm movements. Biol Cybern. 1982;44:67–77.

    Article  PubMed  CAS  Google Scholar 

  2. Hogan N. Mechanical impedance of single and multi-articular systems. In: Winters JM, Woo SL-Y, editors. Multiple muscle systems. New York: Springer; 1990. p. 149–64.

    Chapter  Google Scholar 

  3. Zajac FE. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. CRC Crit Rev Bioeng. 1989;17:359–411.

    CAS  Google Scholar 

  4. Ghez C. The control of movement. In: Kandel ER, Scwartz JH, Jessel TM, editors. Principles of neural science. New York: Elsevier; 1991. p. 533–47.

    Google Scholar 

  5. Bock O. Load compensation in human goal-directed arm movements. Behav Brain Res. 1990;41:167–77.

    Article  PubMed  CAS  Google Scholar 

  6. Lackner JR, DiZio P. Rapid adaptation to Coriolis force perturbations of arm trajectories. J Neurophysiol. 1994;72:299–313.

    PubMed  CAS  Google Scholar 

  7. Sainburg RL, Ghez C, Kalakanis D. Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms. J Neurophysiol. 1999;81(3):1045–56.

    PubMed  CAS  Google Scholar 

  8. Shadmehr R, Mussa-Ivaldi FA. Adaptive representation of dynamics during learning of a motor task. J Neurosci. 1994;14(5):3208–24.

    PubMed  CAS  Google Scholar 

  9. Tee KP, Franklin DW, Kawato M, Milner TE, Burdet E. Concurrent adaptation of force and impedance in the redundant muscle system. Biol Cybern. 2010;102(1):31–44.

    Article  PubMed  Google Scholar 

  10. Franklin DW, So U, Kawato M, Milner TE. Impedance control balances stability with metabolically costly muscle activation. J Neurophysiol. 2004;92(5):3097–105.

    Article  PubMed  Google Scholar 

  11. Osu R, Burdet E, Franklin DW, Milner TE, Kawato M. Different mechanisms involved in adaptation to stable and unstable dynamics. J Neurophysiol. 2003;90(5):3255–69.

    Article  PubMed  Google Scholar 

  12. Thoroughman KA, Shadmehr R. Electromyographic correlates of learning an internal model of reaching movements. J Neurosci. 1999;19(19):8573–88.

    PubMed  CAS  Google Scholar 

  13. Mussa-Ivaldi FA, Patton JL. Robots can teach people how to move their arm. Paper presented at: IEEE international conference on robotics and automation (ICRA). San Francisco; 2000.

    Google Scholar 

  14. Patton JL, Mussa-Ivaldi FA. Robot-assisted adaptive training: custom force fields for teaching movement patterns. IEEE Trans Biomed Eng. 2004;51(4):636–46.

    Article  PubMed  Google Scholar 

  15. Kawato M. Feedback-error-learning neural network for supervised learning. In: Eckmiller R, editor. Advanced neural computers. Amsterdam: North-Holland; 1990. p. 365–72.

    Google Scholar 

  16. Wolpert DM, Ghahramani Z, Jordan MI. An internal model for sensorimotor integration. Science. 1995;269(5232):1880–2.

    Article  PubMed  CAS  Google Scholar 

  17. Flanagan JR, Rao AK. Trajectory adaptation to a nonlinear visuomotor transformation: evidence of motion planning in visually perceived space. J Neurophysiol. 1995;74(5):2174–8.

    PubMed  CAS  Google Scholar 

  18. Scheidt RA, Reinkensmeyer DJ, Conditt MA, Rymer WZ, Mussa-Ivaldi FA. Persistence of motor adaptation during constrained, multi-joint, arm movements. J Neurophysiol. 2000;84(2):853–62.

    PubMed  CAS  Google Scholar 

  19. Patton JL, Kovic M, Mussa-Ivaldi FA. Custom-designed haptic training for restoring reaching ability to individuals with stroke. J Rehabil Res Dev. 2006;43(5):643–56.

    Article  PubMed  Google Scholar 

  20. Patton JL, Stoykov ME, Kovic M, Mussa-Ivaldi FA. Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res. 2006;168(3):368–83.

    Article  PubMed  Google Scholar 

  21. Emken JL, Reinkensmeyer DJ. Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification. IEEE Trans Neural Syst Rehabil Eng. 2005;13(1):33–9.

    Article  PubMed  Google Scholar 

  22. Scheidt RA, Dingwell JB, Mussa-Ivaldi FA. Learning to move amid uncertainty. J Neurophysiol. 2001;86(2):971–85.

    PubMed  CAS  Google Scholar 

  23. Harris C. Perceptual adaptation to inverted, reversed, and displaced vision. Psychol Rev. 1965;72:419–44.

    Article  PubMed  CAS  Google Scholar 

  24. Imamizu H, Miyauchi S, Tamada T, et al. Human cerebellar activity reflecting an acquired internal model of a new tool [see comments]. Nature. 2000;403(6766):192–5.

    Article  PubMed  CAS  Google Scholar 

  25. Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J Neurosci. 2000;20(23):8916–24.

    PubMed  CAS  Google Scholar 

  26. Rossetti Y, Rode G, Pisella L, et al. Prism adaptation to a rightward optical deviation rehabilitates left hemispatial neglect. Nature. 1998;395(6698):166–9.

    Article  PubMed  CAS  Google Scholar 

  27. Shadmehr R, Holcomb HH. Neural correlates of motor memory consolidation. Science. 1997;277:821–5.

    Article  PubMed  CAS  Google Scholar 

  28. Krakauer JW, Ghilardi MF, Ghez C. Independent learning of internal models for kinematic and dynamic control of reaching. Nat Neurosci. 1999;2(11):1026–31.

    Article  PubMed  CAS  Google Scholar 

  29. Tong C, Wolpert DM, Flanagan JR. Kinematics and dynamics are not represented independently in motor working memory: evidence from an interference study. J Neurosci. 2002;22(3):1108–13.

    PubMed  CAS  Google Scholar 

  30. Shadmehr R, Brashers-Krug T. Functional stages in the formation of human long-term motor memory. J Neurosci. 1997;17(1):409–19.

    PubMed  CAS  Google Scholar 

  31. Brashers-Krug T, Shadmehr R, Bizzi E. Consolidation in human motor memory. Nature. 1996;382(6588):252–5.

    Article  PubMed  CAS  Google Scholar 

  32. Weiner MJ, Hallett M, Funkenstein HH. Adaptation to lateral displacement of vision in patients with lesions of the central nervous system. Neurology. 1983;33(6):766–72.

    Article  PubMed  CAS  Google Scholar 

  33. Dancause N, Ptitob A, Levin MF. Error correction strategies for motor behavior after unilateral brain damage: short-term motor learning processes. Neuropsychologia. 2002;40(8):1313–23.

    Article  PubMed  Google Scholar 

  34. Takahashi CG, Reinkensmeyer DJ. Hemiparetic stroke impairs anticipatory control of arm movement. Exp Brain Res. 2003;149:131–40.

    PubMed  Google Scholar 

  35. Beer RF, Given JD, Dewald JPA. Task-dependent weakness at the elbow in patients with hemiparesis. Arch Phys Med Rehabil. 1999;80:766–72.

    Article  PubMed  CAS  Google Scholar 

  36. Wolf SL, Lecraw DE, Barton LA, Jann BB. Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among stroke and head-injured patients. Exp Neurol. 1989;104:125–32.

    Article  PubMed  CAS  Google Scholar 

  37. Flanagan J, Nakano E, Imamizu H, Osu R, Yoshioka T, Kawato M. Composition and decomposition of internal models in motor learning under altered kinematic and dynamic environments. J Neurosci. 1999;19(31–35):RC34.

    PubMed  CAS  Google Scholar 

  38. Wei Y, Patton J. Forces that supplement visuomotor learning: a ‘sensory crossover’ experiment. Paper presented at: Symposium on haptic interfaces, a satellite to the IEEE conference on virtual reality. Chicago; 2004

    Google Scholar 

  39. Chib VS, Patton JL, Lynch KM, Mussa-Ivaldi FA. The effect of stiffness and curvature on the haptic identification of surfaces. Paper presented at: First joint eurohaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems, IEEE-WHC 2005. Pisa; 18–20 Mar 2005.

    Google Scholar 

  40. Heuer H, Rapp K. Active error corrections enhance adaptation to a visuo-motor rotation. Exp Brain Res. 2011;211:97–108.

    Article  PubMed  Google Scholar 

  41. van Asseldonk EH, Wessels M, Stienen AH, van der Helm FC, van der Kooij H. Influence of haptic guidance in learning a novel visuomotor task. J Physiol Paris. 2009;103(3–5):276–85.

    Article  PubMed  Google Scholar 

  42. Liu J, Cramer SC, Reinkensmeyer DJ. Learning to perform a new movement with robotic assistance: comparison of haptic guidance and visual demonstration. J Neuroeng Rehabil. 2006;3:20.

    Article  PubMed  CAS  Google Scholar 

  43. Nudo RJ, Friel KM. Cortical plasticity after stroke: implications for rehabilitation. Rev Neurol. 1999;155(9):713–7.

    PubMed  CAS  Google Scholar 

  44. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533–6.

    Article  Google Scholar 

  45. Thoroughman KA, Shadmehr R. Learning of action through adaptive combination of motor primitives [see comments]. Nature. 2000;407(6805):742–7.

    Article  PubMed  CAS  Google Scholar 

  46. Wolpert DM, Kawato M. Multiple paired forward and inverse models for motor control. Neural Netw. 1998;11(7–8):1317–29.

    Article  PubMed  CAS  Google Scholar 

  47. Brewer B, Klatky R, Matsuoka Y. Perceptual limits for a robotic rehabilitation environment using visual feedback distortion. IEEE Trans Neural Syst Rehabil Eng. 2005;13(1):1–11.

    Article  PubMed  Google Scholar 

  48. Srinivasan MA, LaMotte RH. Tactual discrimination of softness. J Neurophysiol. 1995;73:88–101.

    PubMed  CAS  Google Scholar 

  49. Ernst M, Banks M. Humans integrate visual and haptic information in a statistically optimal fashion. Nature. 2002;415:429–33.

    Article  PubMed  CAS  Google Scholar 

  50. Robles-De-La-Torre G, Hayward V. Force can overcome object geometry in the perception of shape through active touch. Nature. 2001;412:445–8.

    Article  PubMed  CAS  Google Scholar 

  51. Brewer BR, Klatzky R, Matsuoka Y. Effects of visual feedback distortion for the elderly and the motor-impaired in a robotic rehabilitation environment. Paper presented at: IEEE international conference on robotics and automation (ICRA). New Orleans; 2004.

    Google Scholar 

  52. Sainburg RL, Lateiner JE, Latash ML, Bagesteiro LB. Effects of altering initial position on movement direction and extent. J Neurophysiol. 2003;89(1):401–15.

    Article  PubMed  Google Scholar 

  53. Kording KP, Wolpert DM. Bayesian integration in sensorimotor learning. Nature. 2004;427(6971):244–7.

    Article  PubMed  Google Scholar 

  54. Winstein CJ, Merians AS, Sullivan KJ. Motor learning after unilateral brain damage. Neuropsychologia. 1999;37(8):975–87.

    Article  PubMed  CAS  Google Scholar 

  55. Wei Y, Bajaj P, Scheidt RA, Patton JL. A real-time haptic/graphic demonstration of how error augmentation can enhance learning. Paper presented at: IEEE international conference on robotics and automation (ICRA). Barcelona; 2005.

    Google Scholar 

  56. Kording KP, Wolpert DM. The loss function of sensorimotor learning. Proc Natl Acad Sci USA. 2004;101(26):9839–42.

    Article  PubMed  Google Scholar 

  57. Wei Y, Bajaj P, Scheidt RA, Patton JL. Visual error augmentation for enhancing motor learning and rehabilitative relearning. Paper presented at: IEEE-international conference on rehabilitation robotics (ICORR). Chicago; 2005.

    Google Scholar 

  58. Wei K, Kording K. Relevance of error: what drives motor adaptation? J Neurophysiol. 2009;101(2):655–64.

    Article  PubMed  Google Scholar 

  59. Todorov E, Jordan MI. Optimal feedback control as a theory of motor coordination [see comment]. Nat Neurosci. 2002;5(11):1226–35.

    Article  PubMed  CAS  Google Scholar 

  60. Kazerooni H. The human power amplifier technology at the University of California, Berkeley. Robot Autonomous Syst. 1996;19(2):179–87.

    Article  CAS  Google Scholar 

  61. Aguirre-Ollinger G, Colgate JE, Peshkin MA, Goswami A. Active-impedance control of a lower-limb assistive exoskeleton. Paper presented at: IEEE 10th international conference on rehabilitation robotics, 2007. ICORR 2007. Noordwijk; 13–15 June 2007.

    Google Scholar 

  62. Hanlon RE. Motor learning following unilateral stroke. Arch Phys Med Rehabil. 1996;77(8):811–5.

    Article  PubMed  CAS  Google Scholar 

  63. Jarus T, Gutman T. Effects of cognitive processes and task complexity on acquisition, retention, and transfer of motor skills. Can J Occup Ther. 2001;68(5):280–9.

    PubMed  CAS  Google Scholar 

  64. Huang FC, Patton JL, Mussa-Ivaldi FA. Manual skill generalization enhanced by negative viscosity. J Neurophysiol. 2010;104(4):2008–19.

    Article  PubMed  Google Scholar 

  65. Patton JL, Dawe G, Scharver C, Mussa-Ivaldi FA, Kenyon R. Robotics and virtual reality: a perfect marriage for motor control research and rehabilitation. Assist Technol. 2006;18(2):181–95.

    Article  PubMed  Google Scholar 

  66. Patton JL, Wei Y, Scharver C, Kenyon RV, Scheidt R. Motivating rehabilitation by distorting reality. Paper presented at: BioRob 2006: The first IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, Pisa; 20–22 Feb 2006.

    Google Scholar 

  67. Deutsch JE, Merians AS, Adamovich S, Poizner H, Burdea GC. Development and application of virtual reality technology to improve hand use and gait of individuals post-stroke. Restor Neurol Neurosci. 2004;22(3–5):371–86.

    PubMed  Google Scholar 

  68. Burdea GC. Virtual rehabilitation – benefits and ­challenges. Methods Inf Med. 2003;42(5):519–23.

    PubMed  CAS  Google Scholar 

  69. Popescu GV, Burdea G, Boian R. Shared virtual environments for telerehabilitation. Stud Health Technol Inform. 2002;85:362–8.

    PubMed  Google Scholar 

  70. Merians AS, Jack D, Boian R, et al. Virtual reality-augmented rehabilitation for patients following stroke. Phys Ther. 2002;82(9):898–915.

    PubMed  Google Scholar 

  71. Boian R, Sharma A, Han C, et al. Virtual reality-based post-stroke hand rehabilitation. Stud Health Technol Inform. 2002;85:64–70.

    PubMed  CAS  Google Scholar 

  72. Jack D, Boian R, Merians AS, et al. Virtual reality-enhanced stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2001;9(3):308–18.

    Article  PubMed  CAS  Google Scholar 

  73. Burdea G, Popescu V, Hentz V, Colbert K. Virtual reality-based orthopedic telerehabilitation. IEEE Trans Rehabil Eng. 2000;8(3):430–2.

    Article  PubMed  CAS  Google Scholar 

  74. Schultheis M, Rizzo A. The application of virtual reality technology for rehabilitation. Rehabil Psychol. 2001;46:1–16.

    Article  Google Scholar 

  75. Zhang L, Abreu BC, Seale GS, Masel B, Christiansen CH, Ottenbacher KJ. A virtual reality environment for evaluation of a daily living skill in brain injury rehabilitation: reliability and validity. Arch Phys Med Rehabil. 2003;84(8):1118–24.

    Article  PubMed  Google Scholar 

  76. Abdollahi F, Rozario S, Case E, et al. Arm control recovery enhanced by error augmentation. In: IEEE international conference on rehabilitation robotics. Zurich: IEEE; 2011.

    Google Scholar 

  77. Dancausea N, Ptitob A, Levin MF. Error correction strategies for motor behavior after unilateral brain damage: short-term motor learning processes. Neuropsychologia. 2002;40(8):1313–23.

    Article  Google Scholar 

  78. Lisberger S. The neural basis for the learning of simple motor skills. Science. 1988;242(4879):728–35.

    Article  PubMed  CAS  Google Scholar 

  79. Alleva E, Santucci D. Psychosocial vs. “physical” stress situations in rodents and humans: role of neurotrophins. Physiol Behav. 2001;73(3):313–20.

    Article  PubMed  CAS  Google Scholar 

  80. Scheidt RA, Conditt MA, Secco EL, Mussa-Ivaldi FA. Interaction of visual and proprioceptive feedback during adaptation of human reaching movements. J Neurophysiol. 2005;93(6):3200–13.

    Article  PubMed  Google Scholar 

  81. Ravaioli E, Oie KS, Kiemel T, Chiari L, Jeka JJ. Nonlinear postural control in response to visual translation. Exp Brain Res. 2005;160(4):450–9.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by American Heart Association 0330411Z, NIH R24 HD39627, NIH5 R01 NS 35673, NIH F32HD08658, Whitaker RG010157, NSF BES0238442, NIH R01HD053727, NIDRR H133E0700 13 the Summer Internship in Neural Engi­neering (SINE) program at the Sensory Motor Performance Program at the Rehabilitation Institute of Chicago, and the Falk Trust. For additional information see www.SMPP.northwestern.edu/RobotLab.

Author information

Authors and Affiliations

  1. Department of Bioengineering, University of Illinois at Chicago, 851 South Morgan St., 60607-7052, Chicago, IL, USA

    James L. Patton

  2. Sensory Motor Performance Program (SMPP), Rehabilitation Institute of Chicago, Chicago, IL, USA

    James L. Patton

  3. Department of Biomedical Engineering, Northwestern University, 345 E. Superior, #1308, 60611, Chicago, IL, USA

    Felix C. Huang

Authors
  1. James L. Patton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felix C. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James L. Patton .

Editor information

Editors and Affiliations

  1. Uniklinik Balgrist, Forchstr. 340, Zürich, 8008, Switzerland

    Volker Dietz

  2. , ARTORG Center for Biomedical Engineering, Universität Bern, Murtenstrasse 21, Bern, CH-3010, Switzerland

    Tobias Nef

  3. Rehabilitation Institute of Chicago, Illinois, 60611, USA

    William Zev Rymer

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

Patton, J.L., Huang, F.C. (2012). Error Augmentation and the Role of Sensory Feedback. In: Dietz, V., Nef, T., Rymer, W. (eds) Neurorehabilitation Technology. Springer, London. https://doi.org/10.1007/978-1-4471-2277-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2277-7_5

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2276-0

  • Online ISBN: 978-1-4471-2277-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation