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Sensory-Motor Interactions and Error Augmentation

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Neurorehabilitation Technology

Abstract

Brain injury often results a partial loss of the neural resources communicating to the periphery that controls movements. Consequently the signals that were employed prior to injury may no longer be appropriate for controlling the muscles for the intended movement. Hence, a new pattern of signals may need to be learned that appropriately uses the residual resources. The learning required in these circumstances might in fact share features with sports, music performance, surgery, teleoperation, piloting, and child development. Our lab has leveraged key findings in neural adaptation as well as established principles in engineering control theory to develop and test new interactive environments that enhance learning (or relearning). Successful application comes from the use of robotics and video feedback technology to augment error signals. These applications test standing hypotheses about error-mediated neuroplasticity and illustrate an exciting prospect for rehabilitation environments of tomorrow. This chapter highlights our works, identifies our acquired knowledge, and outlines some of the successful pathways for restoring function to brain-injured individuals.

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References

  1. Schmidt R, Lee T. Motor control and learning. A behavioral emphasis. 5th ed. Champaign: Human Kinetics; 2011.

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. 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 

  4. 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 

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

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  8. 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.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  10. 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 

  11. 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 

  12. 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 

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

    CAS  PubMed  Google Scholar 

  14. 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), 2000, San Francisco.

    Google Scholar 

  15. 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 

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

    Chapter  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  18. 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.

    CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  21. 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 

  22. 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 

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

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  25. 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  CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  27. Huang FC, Gillespie RB, Kuo AD. Visual and haptic feedback contribute to tuning and online control during object manipulation. J Mot Behav. 2007;39(3):179–93.

    Article  PubMed  Google Scholar 

  28. Huang FC, Gillespie RB, Kuo AD. Human adaptation to interaction forces in visuo-motor coordination. IEEE Trans Neural Syst Rehabil Eng. 2006;14(3):390–7.

    Article  PubMed  Google Scholar 

  29. Lackner JR, DiZio P. Motor control and learning in altered dynamic environments. Curr Opin Neurobiol. 2005;15(6):653–9.

    Article  CAS  PubMed  Google Scholar 

  30. 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  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  32. 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  CAS  PubMed  Google Scholar 

  33. 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.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  36. 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  CAS  PubMed  Google Scholar 

  37. 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 

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

    PubMed  Google Scholar 

  39. 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  CAS  PubMed  Google Scholar 

  40. 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  CAS  PubMed  Google Scholar 

  41. 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:RC34. 31–35.

    CAS  PubMed  Google Scholar 

  42. 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, 2004, Chicago.

    Google Scholar 

  43. 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), 18–20 March 2005, Pisa.

    Google Scholar 

  44. 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 

  45. 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 

  46. Chib VS, Patton JL, Lynch KM, Mussa-Ivaldi FA. Haptic identification of surfaces as fields of force. J Neurophysiol. 2006;95(2):1068–77.

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  48. Emken JL, Benitez R, Reinkensmeyer DJ. Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed. J Neural Eng Rehabil. 2007;4:8.

    Article  Google Scholar 

  49. Emken JL, Benitez R, Sideris A, Bobrow JE, Reinkensmeyer DJ. Motor adaptation as a greedy optimization of error and effort. J Neurophysiol. 2007;97(6):3997–4006.

    Article  PubMed  Google Scholar 

  50. Hornby TG, Reinkensmeyer DJ, Chen D. Manually-assisted versus robotic-assisted body weight-supported treadmill training in spinal cord injury: what is the role of each? PM & R. J Inj Funct Rehabil. 2010;2(3):214–21.

    Article  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  55. 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 

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

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  58. 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  CAS  PubMed  Google Scholar 

  59. 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), 2004, New Orleans.

    Google Scholar 

  60. 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 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  63. Wei Y, Bajaj P, Scheidt RA, Patton JL. A real-time haptic/graphic demonstration of how error augmentation can enhance learning. IEEE International Conference on Robotics and Automation (ICRA), 2005, Barcelona.

    Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  65. Duarte JE, Gebrekristos B, Perez S, Rowe JB, Sharp K, Reinkensmeyer DJ. Effort, performance, and motivation: insights from robot-assisted training of human golf putting and rat grip strength. Seattle: IEEE International Conference on Rehabilitation Robotics (ICORR); 2013:6650461.

    Google Scholar 

  66. Duarte JE, Reinkensmeyer DJ. Effects of robotically modulating kinematic variability on motor skill learning and motivation. J Neurophysiol. 2015;113(7):2682–91.

    Article  PubMed  PubMed Central  Google Scholar 

  67. 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), 2005, Chicago.

    Google Scholar 

  68. Patton JL, Wei YJ, Bajaj P, Scheidt RA. Visuomotor learning enhanced by augmenting instantaneous trajectory error feedback during reaching. PLoS One. 2013;8(1):e46466.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  71. Kazerooni H. The human power amplifier technology at the University of California. Berkeley Robotics Auton Syst. 1996;19(2):179–87.

    Article  CAS  Google Scholar 

  72. Aguirre-Ollinger G, Colgate JE, Peshkin MA, Goswami A. Active-impedance control of a lower-limb assistive exoskeleton. Noordwijk: International Conference on Rehabilitation Robotics (ICORR); 13–15 June, 2007.

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  74. 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.

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  76. Huang FC, Patton JL. Augmented dynamics and motor exploration as training for stroke. IEEE Trans Biomed Eng. 2013;60(3):838–44.

    Article  PubMed  Google Scholar 

  77. Patton JL, Dawe G, Scharver C, Muss-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 

  78. Patton JL, Wei Y, Scharver C, Kenyon RV, Scheidt R. Motivating rehabilitation by distorting reality. Paper presented at: BioRob: The first IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 20–22 Feb, 2006, Pisa, Tuscany.

    Google Scholar 

  79. Abdollahi F, Rozario S, Case E, et al. Arm control recovery enhanced by error augmentation IEEE International Conference on Rehabilitation Robotics (ICORR), 2011, Zurich.

    Google Scholar 

  80. Abdollahi F, Case ED, Listenberger M, et al. Error augmentation enhancing arm recovery in individuals with chronic hemiparetic stroke: a randomized crossover design. Neurorehab Neural Repair (NNR). 2014;128(2):120–8.

    Google Scholar 

  81. 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 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  84. 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 

  85. 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 

  86. Krebs HI, Palazzolo JJ, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robots. 2003;15(1):7–20.

    Article  Google Scholar 

  87. Kahn LE, Rymer WZ, Reinkensmeyer DJ. Adaptive assistance for guided force training in chronic stroke. San Francisco: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2004.

    Google Scholar 

  88. Kahn LE, Rymer WZ, Reinkensmeyer DJ. Adaptive assistance for guided force training in chronic stroke. Proc Annu Int Conf IEEE Eng Med Biol Conf (EMBC). 2004;4:2722–5.

    Article  CAS  Google Scholar 

  89. Colombo R, Sterpi I, Mazzone A, Delconte C, Pisano F. Taking a lesson from patients’ recovery strategies to optimize training during robot-aided rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2012;20(3):276–85.

    Article  PubMed  Google Scholar 

  90. Kahn LE, Lum PS, Rymer WZ, Reinkensmeyer DJ. Robot-assisted movement training for the stroke-impaired arm: does it matter what the robot does? J Rehabil Res Dev. 2006;43(5):619–30.

    Article  PubMed  Google Scholar 

  91. Johnson MJ, Van der Loos HF, Burgar CG, Shor P, Leifer LJ. Experimental results using force-feedback cueing in robot-assisted stroke therapy. IEEE Trans Neural Syst Rehabil Eng. 2005;13(3):335–48.

    Article  PubMed  Google Scholar 

  92. Wright ZA, Fisher ME, Huang FC, Patton JL. Data sample size needed for prediction of movement distributions. Conf Proc IEEE Eng Med Biol Soc (EMBC). 2014;2014:5780–3.

    Google Scholar 

  93. Sundaram H, Chen Y, Rikakis T. A computational framework for constructing interactive feedback for assisting motor learning. Conf Proc IEEE Eng Med Biol Soc (EMBC). 2011;2011:1399–402.

    Google Scholar 

  94. Wei Y, Kording KP. Relevance of error: what drives motor adaptation? J Neurophys. 2009;101(2):655–4.

    Article  Google Scholar 

  95. Herzfeld DJ, Shadmehr R. Motor variability is not noise, but grist for the learning mill. Nat Neurosci. 2014;17(2):149–50.

    Article  CAS  PubMed  Google Scholar 

  96. Herzfeld DJ, Vaswani PA, Marko MK, Shadmehr R. A memory of errors in sensorimotor learning. Science. 2014;12(345):1349–53.

    Article  Google Scholar 

  97. Takiyama K, Hirashima M, Nozaki D. Prospective errors determine motor learning. Nat Commun. 2015;6:5925.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Fisher ME, Huang FC, Wright ZA, Patton JL. Distributions in the error space: goal-directed movements described in time and state-space representations. Conf Proc IEEE Eng Med Biol Soc (EMBC). 2014;2014:6953–6.

    Google Scholar 

  99. Fisher ME, Huang FC, Klamroth-Marganska V, Riener R, Patton JL. Haptic error fields for robotic training. Evanston: World Haptics Conference (WHC); 2015.

    Google Scholar 

  100. Buch ER, Modir Shanechi A, Fourkas AD, Weber C, Birbaumer N, Cohen LG. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain. 2012;135(Pt 2):596–614.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Parry R, Lincoln N, Vass C. Effect of severity of arm impairment on response to additional physiotherapy early after stroke. Clin Rehabil. 1999;13(3):187–98.

    Article  CAS  PubMed  Google Scholar 

  102. Winstein CJ, Rose DK, Tan SM, Lewthwaite R, Chui HC, Azen SP. A randomized controlled comparison of upper-extremity rehabilitation strategies in acute stroke: a pilot study of immediate and long-term outcomes. Arch Phys Med Rehabil. 2004;85(4):620–8.

    Article  PubMed  Google Scholar 

  103. Nowak DA, Grefkes C, Ameli M, Fink GR. Interhemispheric competition after stroke: brain stimulation to enhance recovery of function of the affected hand. Neurorehabil Neural Repair (NNR). 2009;23(7):641.

    Article  Google Scholar 

  104. Parmar PN, Patton JL. Optimal gain schedules for visuomotor skill training using error-augmented feedback. Seattle: Proceedings 2015 IEEE International Conference on Robotics and Automation (ICRA), 26–30 May, 2015.

    Google Scholar 

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Acknowledgments

This work was supported by American Heart Association 0330411Z, NIH R24 HD39627, NIH 5 RO1 NS 35673, NIH F32HD08658, Whitaker RG010157, NSF BES0238442, NIH R01HD053727, 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.

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Authors and Affiliations

  1. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA

    James L. Patton PhD & Felix C. Huang PhD

  2. Department of Physical Medicine Rehabilitation, Northwestern University, Chicago, IL, USA

    James L. Patton PhD & Felix C. Huang PhD

  3. Department of Bioengineering, Universities of Illinois at Chicago, Chicago, IL, USA

    James L. Patton PhD

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  1. James L. Patton PhD
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  2. Felix C. Huang PhD
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Correspondence to James L. Patton PhD .

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Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Department of Anatomy and Neurobiology, Department of Biomedical Engineering, Department of Physical Medicine and Rehabilitation, University of California at Irvine, Irvine, California, USA

    David J. Reinkensmeyer

  2. Spinal Cord Injury Center, University Hospital Balgrist, Zürich, Switzerland

    Volker Dietz

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Patton, J.L., Huang, F.C. (2016). Sensory-Motor Interactions and Error Augmentation. In: Reinkensmeyer, D., Dietz, V. (eds) Neurorehabilitation Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-28603-7_5

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