Abstract
This investigation is one in a series of studies that address the possibility of stroke rehabilitation using robotic devices to facilitate “adaptive training.” Healthy subjects, after training in the presence of systematically applied forces, typically exhibit a predictable “after-effect.” A critical question is whether this adaptive characteristic is preserved following stroke so that it might be exploited for restoring function. Another important question is whether subjects benefit more from training forces that enhance their errors than from forces that reduce their errors. We exposed hemiparetic stroke survivors and healthy age-matched controls to a pattern of disturbing forces that have been found by previous studies to induce a dramatic adaptation in healthy individuals. Eighteen stroke survivors made 834 movements in the presence of a robot-generated force field that pushed their hands proportional to its speed and perpendicular to its direction of motion — either clockwise or counterclockwise. We found that subjects could adapt, as evidenced by significant after-effects. After-effects were not correlated with the clinical scores that we used for measuring motor impairment. Further examination revealed that significant improvements occurred only when the training forces magnified the original errors, and not when the training forces reduced the errors or were zero. Within this constrained experimental task we found that error-enhancing therapy (as opposed to guiding the limb closer to the correct path) to be more effective than therapy that assisted the subject.
Similar content being viewed by others
References
Bastian AJ, Martin TA, Keating JG, Thach WT (1996) Cerebellar ataxia: abnormal control of interaction torques across multiple joints. J Neurophysiol 76:492–509
Beer RF, Dewald JPA, Rymer WZ (2000) Deficits in the coordination of multijoint arm movements in patients with hemiparesis. Exp Brain Res 131:305–319
Beer RF, Given JD, Dewald JPA (1999) Task-dependent weakness at the elbow in patients with hemiparesis. Arch Phys Med Rehabil 80:766–772
Bhushan N, Shadmehr R (1999) Computational nature of human adaptive control during learning of reaching movements in force fields. Biol Cybern 81:39–60
Bobath B (1978) Adult hemiplegia: evaluation and treatment. Heinemann, London
Bock O (1990) Load compensation in human goal-directed arm movements. Behav Brain Res 41:167–177
Boyd LA, Winstein CJ (2001) Implicit motor-sequence learning in humans following unilateral stroke: the impact of practice and explicit knowledge. Neurosci Lett 298:65–69
Boyd LA, Winstein CJ (2003) Impact of explicit information on implicit motor-sequence learning following middle cerebral artery stroke. Phys Ther 83:976–989
Boyd LA, Winstein CJ (2004) Providing explicit information disrupts implicit motor learning after basal ganglia stroke. Learn Mem 11:388–396
Brewer B, Klatky R, Matsuoka Y (2005) Perceptual limits for a robotic rehabilitation environment using visual feedback distortion. IEEE Trans Neural Sys Rehabil Eng in press
Brewer BR, Klatzky R, Matsuoka Y (2004) Effects of visual feedback distortion for the elderly and the motor-impaired in a robotic rehabilitation environment. In: IEEE International conference on robotics and automation (ICRA), New Orleans
Burgar C, Lum P, Shor P, Van der Loos H (2000) Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J Rehabil Res Dev 376:663–673
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 78:554–560
Conditt MA, Mussa-Ivaldi FA (1999) Central representation of time during motor learning. Proc Nat Acad Sci 96:11625–11630
Dancausea N, Ptitob A, Levin MF (2002) Error correction strategies for motor behavior after unilateral brain damage: short-term motor learning processes. Neuropsychologia 40:1313–1323
Delisa JA, Gans BM (eds) (1993) Rehabilitation medicine. J. B. Lippincott Company, Philadelphia
Dewald J, Beer R (2001) Evidence for abnormal joint torque patterns in the paretic upper limb of subjects with hemiparesis. Muscle Nerve 24:273–283
Dewhurst DJ (1967) Neuromuscular control system. IEEE Trans Biomed Eng 14:167–171
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 in press
Ernst M, Banks M (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:429–433
Fasoli SE, Krebs HI, Ferraro M, Hogan N, Volpe BT (2004) Does shorter rehabilitation limit potential recovery poststroke? Neurorehabil Neural Repair 18:88–94
Fisk JD, Goodale MA (1988) The effects of unilateral brain damage on visually guided reaching: hemispheric differences in the nature of the deficit. Exp Brain Res 72:425–435
Fitts PM (1964) Perceptual-motor skill learning. In: MeHou AW (ed) Categories of human learning. Academic, New York, pp 243–245
Flanagan JR, Rao AK (1995) Trajectory adaptation to a nonlinear visuomotor transformation: evidence of motion planning in visually perceived space. J Neurophysiol 74:2174–2178
Flash T, Gurevitch F (1992) Arm movement and stiffness adaptation to external loads. In: Proceedings of the 13th IEEE engineering in medicine and biology conference, vol 13. Orlando, pp 885–886
Gandolfo F, Mussa-Ivaldi FA, Bizzi E (1996) Motor learning by field approximation. Proc Nat Acad Sci USA 93:3843–3846
Ghez (1991) The control of movement. In: Kandel ER, Scwartz JH, Jessel TM (eds) Principles of neural science. Appleton & Lange, Nor wolk, pp 533–547
Gomez Beldarrain M, Grafman J, Pascual-Leone A, Garcia-Monco JC (1999) Procedural learning is impaired in patients with prefrontal lesions. Neurology 52:1853–1860
Held R, Freedman SJ (1963) Plasticity in Human Sensorimotor Control. Science 142:455–461
Hemami H, Stokes BT (1982) Qualitative discussion of mechanisms of feedback and feedforward control of locomotion. IEEE Trans Biomed Eng 30:163–189
Kahn LE, Zygman ML, Rymer WZ, Reinkensmeyer DJ (2001) Effect of robot-assisted and enassisted exercise on functional reaching in chronic hemiparesis. In: IEEE engineering in medicine and biology society (EMBS) conference. IEEE, Istanbul, pp 1344–1347
Kawato M (1990) Feedback-error-learning neural network for supervised learning. In: Eckmiller R (ed) Advanced neural computers. North-Holland, Amsterdam, pp 365–372
Kawato M, Wolpert D (1998) Internal models for motor control. Novartis Foundation Symposium 218:291–304
Kording KP, Wolpert DM (2004a) Bayesian integration in sensorimotor learning. Nature 427:244–247
Kording KP, Wolpert DM (2004b) The loss function of sensorimotor learning. Proc Natl Acad Sci USA 101:9839–9842
Krakauer JW, Ghilardi MF, Ghez C (1999) Independent learning of internal models for kinematic and dynamic control of reaching. Nat Neurosci 2:1026–1031
Krebs HI, Aisen ML, Volpe BT, Hogan N (1999a) Quantization of continuous arm movements in humans with brain injury. Proc Nat Acad Sci USA 96:4645–4649
Krebs HI, Brashers-Krug T, Rauch SL, Savage CR, Hogan N, Rubin RH, Fischman AJ, Alpert NM (1998a) Robot-aided functional imaging: application to a motor learning study. Hum Brain Mapp 6:59–72
Krebs HI, Hogan N, Aisen ML, Volpe BT (1998b) Robot-aided neurorehabilitation. IEEE Trans Rehabil Eng 6:75–87
Krebs HI, Hogan N, Hening W, Adamovich SV, Poizner H (2001) Procedural motor learning in Parkinson’s disease. Exp Brain Res 141:425–437
Krebs HI, Hogan N, Volpe BT, Aisen ML, Edelstein L, Diels C (1999b) Robot-aided Neuro-rehabilitation in Stroke: Three Year follow-up. In: Sixth international conference of rehabilitation robotics, Stanford
Krebs HI, Volpe BT, Aisen ML, Hogan N (2000) Increasing productivity and quality of care: robot-aided neuro-rehabilitation. J Rehabil Res Dev 37:639–652
Lackner JR, DiZio P (1994) Rapid adaptation to Coriolis force perturbations of arm trajectories. J Neurophysiol 72:299–313
Lisberger S (1988) The neural basis for the learning of simple motor skills. Science 242:728–735
Lum P, Burgar C, Shor P, Majmundar M, Van der Loos M (2002) Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper limb motor function following stroke. Arch Phys Med Rehabil 83:952–959
Lum PS, Burgar CG, Kenney DE, Van der Loos HF (1999) Quantification of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis. IEEE Trans Biomed Eng 46:652–662
Miall RC, Weir DJ, Wolpert DM, Stein JF (1993) Is the cerebellum a Smith Predictor? J Motor Behav 25:203–216
Mussa-Ivaldi FA, Patton JL (2000) Robots can teach people how to move their arm. In: IEEE international conference on robotics and automation (ICRA), San Francisco
Patton J, Mussa-Ivaldi F (eds) (2001) Robotic teaching by exploiting the nervous system’s adaptive mechanisms. IOS, Amsterdam
Patton J, Mussa-Ivaldi F, Rymer W (2001a) Altering Movement Patterns in Healthy and Brain-Injured Subjects Via Custom Designed Robotic Forces. In: IEEE-EMBC2001, the 23rd annual international conference of the ieee engineering in medicine and biology society (EMBS), Istanbul
Patton J, Mussa-Ivaldi F, Rymer W (2001b) Robotic-induced improvement of movements in hemiparetics via an implicit learning technique. Society for Neuroscience, San Diego
Patton JL, Mussa-Ivaldi FA (2003) Robot-assisted adaptive training: custom force fields for teaching movement patterns. IEEE Trans Biomed Eng (in press)
Pine ZM, Krakauer JW, Gordon J, Ghez C (1996) Learning of scaling factors and reference axes for reaching movements. Neuroreport 7:2357–2361
Pohl PS, Winstein CJ (1999) Practice effects on the less-affected upper extremity after stroke. Arch Phys Med Rehabil 80:668–675
Raasch CC, Mussa-Ivaldi FA, Rymer WZ (1997) Motor Learning in reaching movements by hemiparetic subjects. Soc Neurosci Abstr 924.17:2374
Reinkensmeyer DJ, Iobbi MG, Kahn LE, Kamper DG, Takahashi CD (2003) Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural firing-rate variability. Neural Comput 15:2619–2642
Robles-De-La-Torre G, Hayward V (2001) Force can overcome object geometry in the perception of shape through active touch. Nature 412:445–448
Rossetti Y, Rode G, Pisella L, Farne A, Li L, Boisson D, Perenin MT (1998) Prism adaptation to a rightward optical deviation rehabilitates left hemispatial neglect. Nature 395:166–169
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature (London) 323:533–536
Rumelhart DE, McClelland JL (eds) (1986) Parallel distributed processing: explorations in the microstructure of cognition. MIT, Cambridge
Sainburg RL, Lateiner JE, Latash ML, Bagesteiro LB (2003) Effects of altering initial position on movement direction and extent. J Neurophysiol 89:401–415
Sanes JN, Dimitrov B, Hallett M (1990) Motor learning in patients with cerebellar dysfunction. Brain 113:103–120
Scheidt RA, Dingwell JB, Mussa-Ivaldi FA (2001) Learning to move amid uncertainty. J Neurophysiol 86:971–985
Scheidt RA, Reinkensmeyer DJ, Conditt MA, Rymer WZ, Mussa-Ivaldi FA (2000) Persistence of motor adaptation during constrained, multi-joint, arm movements. J Neurophysiol 84:853–862
Scheidt RA, Rymer WZ (2000) Control Strategies for the Transition From Multijoint to Single-Joint Arm Movements Studied Using a Simple Mechanical Constraint. J Neurophysiol 83:1–12
Schmidt RA (1988) Motor control and learning. Human kinetics Publishers, Champaign
Schmit BD, Dewald JP, Rymer WZ (2000) Stretch reflex adaptation in elbow flexors during repeated passive movements in unilateral brain-injured patients. Arch Phys Med Rehabil 81:269–278
Seidler RD, Purushotham A, Kim SG, Ugurbil K, Willingham D, Ashe J (2002) Cerebellum activation associated with performance change but not motor learning.[see comment]. Science 296:2043–2046
Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14:3208–3224
Squire LR (1986) Mechanisms of memory. Science 232:1612–1619
Srinivasan MA, LaMotte RH (1995) Tactual discrimination of softness. In: J Neurophysiol, vol 73, pp 88–101
Stein J, Krebs HI, Frontera WR, Fasoli SE, Hughes R, Hogan N (2004) Comparison of two techniques of robot-aided upper limb exercise training after stroke. Am J Phys Med Rehabil 83:720–728
Takahashi CD, Scheidt RA, Reinkensmeyer DJ (2001) Impedance control and internal model formation when reaching in a randomly varying dynamical environment. J Neurophysiol 86:1047–1051
Takahashi CG, Reinkensmeyer DJ (2003) Hemiparetic stroke impairs anticipatory control of arm movement. Exp Brain Res 149:131–140
Taub E (2000) Constraint-induced movement therapy and massed practice [letter; comment]. Stroke 31:986–988
Taub E, Miller N, Novack T, Cook Ed, Fleming W, Nepomuceno C, Connell J, Crago J (1993) Technique to improve chronic motor deficit after stroke. Arch phys med rehabil 74:347–354
Taub E, Uswatte G, Pidikiti R (1999) Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation–a clinical review. [see comments]. J Rehabil Res Dev 36:237–251
Thoroughman KA, Shadmehr R (2000) Learning of action through adaptive combination of motor primitives. [see comments]. Nature 407:742–747
Volpe B, Krebs H, Hogan N, Edelstein L, Diels M, Aisen M (2000) A novel approach to stroke rehabilitation; robot-aided sensorimotor stimulation. Neurology 54:1938–1944
Volpe BT, Krebs HI, Hogan N (2001) Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? Curr Opin Neurol 14:745–752
Volpe BT, Krebs HI, Hogan N, Edelsteinn L, Diels CM, Aisen ML (1999) Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology 53:1874–1876
Voss DR, Ionta MK, Myers BJ (1985) Proprioceptive neuromuscular facilitation. Harper and Row, Philadelphia
Wei Y, Bajaj P, Scheidt RA, Patton JL (2005) A real-time haptic/graphic demonstration of how error augmentation can enhance learning. In: IEEE international conference on robotics and automation (ICRA), Barcelona
Weiner MJ, Hallett M, Funkenstein HH (1983) Adaptation to lateral displacement of vision in patients with lesions of the central nervous system. Neurology 33:766–772
Winstein CJ, Merians AS, Sullivan KJ (1999) Motor learning after unilateral brain damage. Neuropsychologia 37:975–987
Wolf SL, Lecraw DE, Barton LA, Jann BB (1989) Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among stroke and head-injured patients. Exp Neurol 104:125–132
Acknowledgements
Supported by AHA 0330411Z, NIH 1 R24 HD39627-01, NINDS R01 NS35673.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Patton, J.L., Stoykov, M.E., Kovic, M. et al. Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res 168, 368–383 (2006). https://doi.org/10.1007/s00221-005-0097-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00221-005-0097-8