Beyond Human or Robot Administered Treadmill Training

  • Hermano Igo Krebs
  • Konstantinos Michmizos
  • Tyler Susko
  • Hyunglae Lee
  • Anindo Roy
  • Neville Hogan


The demand for rehabilitation services is growing apace with the graying of the population. This situation creates both a need and an opportunity to deploy technologies such as rehabilitation robotics, and in the last decade and half, several research groups have deployed variations of this technology. Results so far are mixed with the available evidence demonstrating unequivocally that some forms of robotic therapy can be highly effective, even for patients many years post-stroke, while other forms of robotic therapy have been singularly ineffective. The contrast is starkest when we contrast upper-extremity and lower-extremity therapy. In fact, 2010 Stroke Care Guidelines of the American Heart Association (AHA) and of the Veterans Administration/Department of Defense (VA/DoD) endorsed the use of the rehabilitation robotics for upper-extremity post-stroke care, but concluded that lower-extremity robotic therapy is much less effective as compared to usual care practices in the USA and declared “still in its infancy.” We submit that the contrasting effectiveness of upper- and lower-extremity therapies arises from neural factors, not technological factors. Though, no doubt, it might be improved, the technology deployed to date for locomotor therapy is elegant and sophisticated. Unfortunately, it may be misguided, providing highly repeatable control of rhythmic movement but ultimately doing the wrong thing. The technology we have deployed to date for upper-extremity therapy is firmly based on an understanding of how upper-extremity behavior is neurally controlled and derived from decades of neuroscience research. The limitations of lower-extremity robotic therapy lie not in the robotic technology but in its incompatibility with human motor neuroscience. In this chapter we briefly review the evidence supporting such negative views, and based on our experience with upper-extremity robotic therapy, we describe what we are presently investigating to revert and work toward a future endorsement of the AHA and VA/DoD for rehabilitation robotics for lower-extremity post-stroke care.


Rehabilitation robotics Robot-assisted therapy Robotic therapy Anklebot MIT-Skywalker Lower extremity Stroke Cerebral palsy 


  1. 1.
    Miller EL, Murray L, Richards L, Zorowitz RD, Bakas T, Clark P, Billinger SA, N. American Heart Association Council on Cardiovascular, C. the Stroke. Comprehensive overview of nursing and interdisciplinary rehabilitation care of the stroke patient: a scientific statement from the American Heart Association. Stroke. 2010;41:2402–48.PubMedCrossRefGoogle Scholar
  2. 2.
    G. Management of Stroke Rehabilitation Working. VA/DOD clinical practice guideline for the management of stroke rehabilitation. J Rehabil Res Dev. 2010;47:1–43.CrossRefGoogle Scholar
  3. 3.
    Mehrholz J, Elsner B, Werner C, Kugler J, Pohl M. Electromechanical-assisted training for walking after stroke: updated evidence. Stroke. 2013;44:e127–8.PubMedCrossRefGoogle Scholar
  4. 4.
    Hornby TG, Campbell DD, Kahn JH, Demott T, Moore JL, Roth HR. Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study. Stroke. 2008;39:1786–92.PubMedCrossRefGoogle Scholar
  5. 5.
    Hidler J, Nichols D, Pelliccio M, Brady K, Campbell DD, Kahn JH, Hornby TG. Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke. Neurorehabil Neural Repair. 2009;23:5–13.PubMedCrossRefGoogle Scholar
  6. 6.
    Dietz V. Spinal cord pattern generators for locomotion. Clin Neurophysiol. 2003;114:1379–89.PubMedCrossRefGoogle Scholar
  7. 7.
    Duncan PW, Sullivan KJ, Behrman AL, Azen SP, Wu SS, Nadeau SE, Dobkin BH, Rose DK, Tilson JK, Cen S, Hayden SK, Team LI. Body-weight-supported treadmill rehabilitation after stroke. N Engl J Med. 2011;364:2026–36.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Dobkin BH, Duncan PW. Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot back to the starting gate? Neurorehabil Neural Repair. 2012;26:308–17.PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Hogan N, Sternad D. Dynamic primitives in the control of locomotion. Front Comput Neurosci. 2013;7:71.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Hogan N, Sternad D. On rhythmic and discrete movements: reflections, definitions and implications for motor control. Exp Brain Res. 2007;181:13–30.PubMedCrossRefGoogle Scholar
  11. 11.
    Mussa-Ivaldi FA, Hogan N, Bizzi E. Neural, mechanical, and geometric factors subserving arm posture in humans. J Neurosci. 1985;5:2732–43.PubMedGoogle Scholar
  12. 12.
    Schaal S, Sternad D, Osu R, Kawato M. Rhythmic arm movement is not discrete. Nat Neurosci. 2004;7:1136–43.PubMedCrossRefGoogle Scholar
  13. 13.
    Ikegami T, Hirashima M, Taga G, Nozaki D. Asymmetric transfer of visuomotor learning between discrete and rhythmic movements. J Neurosci. 2010;30:4515–21.PubMedCrossRefGoogle Scholar
  14. 14.
    Forrester LW, Roy A, Krebs HI, Macko RF. Ankle training with a robotic device improves hemiparetic gait after a stroke. Neurorehabil Neural Repair. 2011;25(4):369–77.PubMedCrossRefGoogle Scholar
  15. 15.
    Forster A, Young J. Incidence and consequences of falls due to stroke: a systematic inquiry. BMJ. 1995;311:83–6.PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Ramnemark A, Nyberg L, Borssen B, Olsson T, Gustafson Y. Fractures after stroke. Osteoporos Int. 1998;8:92–5.PubMedCrossRefGoogle Scholar
  17. 17.
    Dennis MS, Lo KM, McDowall M, West T. Fractures after stroke: frequency, types, and associations. Stroke. 2002;33:728–34.PubMedCrossRefGoogle Scholar
  18. 18.
    Kanis J, Oden A, Johnell O. Acute and long-term increase in fracture risk after hospitalization for stroke. Stroke. 2001;32:702–6.PubMedCrossRefGoogle Scholar
  19. 19.
    Duncan PW, Sullivan KJ, Behrman AL, Azen SP, Wu SS, Nadeau SE, Dobkin BH, Rose DK, Tilson JK, Team LI. Protocol for the Locomotor Experience Applied Post-stroke (LEAPS) trial: a randomized controlled trial. BMC Neurol. 2007;7:39.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Hogan N, Sternad D. Dynamic primitives of motor behavior. Biol Cybern. 2012;106:727–39.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Roy A, Krebs HI, Williams DJ, Bever CT, Forrester LW, Macko RM, Hogan N. Robot-aided neurorehabilitation: a novel robot for ankle rehabilitation. Robot IEEE Trans. 2009;25:569–82.CrossRefGoogle Scholar
  22. 22.
    Susko TG and Massachusetts Institute of Technology. Department of mechanical engineering. MIT Skywalker: a novel robot for gait rehabilitation of stroke and cerebral palsy patients.Google Scholar
  23. 23.
    Cai LL, Fong AJ, Otoshi CK, Liang Y, Burdick JW, Roy RR, Edgerton VR. Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning. J Neurosci. 2006;26:10564–8.PubMedCrossRefGoogle Scholar
  24. 24.
    Michmizos K, Rossi S, Castelli E, Cappa P, Krebs H. Robot-aided neurorehabilitation: a pediatric robot for ankle rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2015;23(6):1056–67.PubMedCrossRefGoogle Scholar
  25. 25.
    Robertson DG, Winter DA. Mechanical energy generation, absorption and transfer amongst segments during walking. J Biomech. 1980;13:845–54.PubMedCrossRefGoogle Scholar
  26. 26.
    Sawicki GS, Ferris DP. A pneumatically powered knee-ankle-foot orthosis (KAFO) with myoelectric activation and inhibition. J Neuroeng Rehabil. 2009;6:23.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Eng JJ, Winter DA. Kinetic analysis of the lower limbs during walking: what information can be gained from a three-dimensional model? J Biomech. 1995;28:753–8.PubMedCrossRefGoogle Scholar
  28. 28.
    Teixeira-Salmela LF, Nadeau S, Milot MH, Gravel D, Requiao LF. Effects of cadence on energy generation and absorption at lower extremity joints during gait. Clin Biomech (Bristol, Avon). 2008;23:769–78.CrossRefGoogle Scholar
  29. 29.
    Umberger BR, Martin PE. Mechanical power and efficiency of level walking with different stride rates. J Exp Biol. 2007;210:3255–65.PubMedCrossRefGoogle Scholar
  30. 30.
    Perry J, Davids JR. Gait analysis: normal and pathological function. J Pediatr Orthop. 1992;12(6):815.41Google Scholar
  31. 31.
    Neptune RR, Kautz SA, Zajac FE. Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking. J Biomech. 2001;34:1387–98.PubMedCrossRefGoogle Scholar
  32. 32.
    Meinders M, Gitter A, Czerniecki JM. The role of ankle plantar flexor muscle work during walking. Scand J Rehabil Med. 1998;30:39–46.PubMedCrossRefGoogle Scholar
  33. 33.
    Gottschall JS, Kram R. Energy cost and muscular activity required for propulsion during walking. J Appl Physiol (1985). 2003;94:1766–72.CrossRefGoogle Scholar
  34. 34.
    Roy A, Krebs HI, Williams DJ, Bever CT, Forrester LW, Macko RM, Hogan N. Robot-aided neurorehabilitation: a novel robot for ankle rehabilitation. IEEE Trans Robot. 2009;25:569–82.CrossRefGoogle Scholar
  35. 35.
    Khanna I, Roy A, Rodgers MM, Krebs HI, Macko RM, Forrester LW. Effects of unilateral robotic limb loading on gait characteristics in subjects with chronic stroke. J Neuroeng Rehabil. 2010;7:23.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Rossi S, Colazza A, Petrarca M, Castelli E, Cappa P, Krebs HI. Feasibility study of a wearable exoskeleton for children: is the gait altered by adding masses on lower limbs? PLoS One. 2013;8:e73139.PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Collins S, Ruina A, Tedrake R, Wisse M. Efficient bipedal robots based on passive-dynamic walkers. Science. 2005;307:1082–5.PubMedCrossRefGoogle Scholar
  38. 38.
    Bosecker CK, MIT-Skywalker HI. Proc IEEE 11th international conference on rehabilitation robotics. 2009:542–9.Google Scholar
  39. 39.
    Artemiadis PK, Krebs HI. On the control of the MIT-skywalker. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:1287–91.PubMedGoogle Scholar
  40. 40.
    Kim SJ, Krebs HI. Effects of implicit visual feedback distortion on human gait. Exp Brain Res. 2012;218:495–502.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Seiterle S, Susko T, Artemiadis PK, Riener R, Krebs H.I. Interlimb coordination in body-weight supported locomotion: a pilot study. J Biomech. 2015;48:2837–43.Google Scholar
  42. 42.
    Bernshtein NA. The co-ordination and regulation of movements. 1st ed. Oxford: Pergamon Press; 1967.Google Scholar
  43. 43.
    Bizzi E, Tresch MC, Saltiel P, d’Avella A. New perspectives on spinal motor systems. Nat Rev Neurosci. 2000;1:101–8.PubMedCrossRefGoogle Scholar
  44. 44.
    Grillner S, Wallen P. Central pattern generators for locomotion, with special reference to vertebrates. Annu Rev Neurosci. 1985;8:233–61.PubMedCrossRefGoogle Scholar
  45. 45.
    Bussel B, Roby-Brami A, Neris OR, Yakovleff A. Evidence for a spinal stepping generator in man. Paraplegia. 1996;34:91–2.PubMedCrossRefGoogle Scholar
  46. 46.
    Brown TG. The intrinsic factors in the act of progression in the mammal. Proc R Soc Lond Ser B Containing Pap Biol Charact. 1911;84:308–19.CrossRefGoogle Scholar
  47. 47.
    Duysens J, Van de Crommert HW. Neural control of locomotion: the central pattern generator from cats to humans. Gait Posture. 1998;7:131–41.PubMedCrossRefGoogle Scholar
  48. 48.
    Grasso R, Ivanenko YP, Zago M, Molinari M, Scivoletto G, Castellano V, Macellari V, Lacquaniti F. Distributed plasticity of locomotor pattern generators in spinal cord injured patients. Brain. 2004;127:1019–34.PubMedCrossRefGoogle Scholar
  49. 49.
    Ivanenko YP, Cappellini G, Dominici N, Poppele RE, Lacquaniti F. Modular control of limb movements during human locomotion. J Neurosci. 2007;27:11149–61.PubMedCrossRefGoogle Scholar
  50. 50.
    Scivoletto G, Ivanenko Y, Morganti B, Grasso R, Zago M, Lacquaniti F, Ditunno J, Molinari M. Plasticity of spinal centers in spinal cord injury patients: new concepts for gait evaluation and training. Neurorehabil Neural Repair. 2007;21:358–65.PubMedCrossRefGoogle Scholar
  51. 51.
    Dominici N, Ivanenko YP, Cappellini G, d’Avella A, Mondì V, Cicchese M, Fabiano A, Silei T, Di Paolo A, Giannini C, Poppele RE, Lacquaniti F. Locomotor primitives in newborn babies and their development. Science. 2011;334:997–9.PubMedCrossRefGoogle Scholar
  52. 52.
    Calancie B, Needham-Shropshire B, Jacobs P, Willer K, Zych G, Green BA. Involuntary stepping after chronic spinal cord injury. Evidence for a central rhythm generator for locomotion in man. Brain. 1994;117(Pt 5):1143–59.PubMedCrossRefGoogle Scholar
  53. 53.
    Sinkjaer T, Andersen JB, Ladouceur M, Christensen LO, Nielsen JB. Major role for sensory feedback in soleus EMG activity in the stance phase of walking in man. J Physiol. 2000;523(Pt 3):817–27.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Nakazawa K, Kawashima N, Akai M, Yano H. On the reflex coactivation of ankle flexor and extensor muscles induced by a sudden drop of support surface during walking in humans. J Appl Physiol (1985). 2004;96:604–11.CrossRefGoogle Scholar
  55. 55.
    van der Linden MH, Marigold DS, Gabreels FJ, Duysens J. Muscle reflexes and synergies triggered by an unexpected support surface height during walking. J Neurophysiol. 2007;97:3639–50.PubMedCrossRefGoogle Scholar
  56. 56.
    Af Klint R, Nielsen JB, Sinkjaer T, Grey MJ. Sudden drop in ground support produces force-related unload response in human overground walking. J Neurophysiol. 2009;101:1705–12.PubMedCrossRefGoogle Scholar
  57. 57.
    Marigold DS, Patla AE. Adapting locomotion to different surface compliances: neuromuscular responses and changes in movement dynamics. J Neurophysiol. 2005;94:1733–50.PubMedCrossRefGoogle Scholar
  58. 58.
    Duysens J, Bastiaanse CM, Smits-Engelsman BC, Dietz V. Gait acts as a gate for reflexes from the foot. Can J Physiol Pharmacol. 2004;82:715–22.PubMedCrossRefGoogle Scholar
  59. 59.
    Nielsen JB. How we walk: central control of muscle activity during human walking. Neuroscientist. 2003;9:195–204.PubMedCrossRefGoogle Scholar
  60. 60.
    Bonnet M, Gurfinkel S, Lipchits MJ, Popov KE. Central programming of lower limb muscular activity in the standing man. Agressologie. 1976;17(SPECNO):35–42.PubMedGoogle Scholar
  61. 61.
    Debaere F, Swinnen SP, Beatse E, Sunaert S, Van Hecke P, Duysens J. Brain areas involved in interlimb coordination: a distributed network. Neuroimage. 2001;14:947–58.PubMedCrossRefGoogle Scholar
  62. 62.
    Netter FH. Atlas of human anatomy. 6th ed. Philadelphia: Saunders/Elsevier; 2014.Google Scholar
  63. 63.
    Waugh A, Grant A. Ross and Wilson anatomy and physiology in health and illness. 12th ed. Edinburgh: Elsevier; 2014.Google Scholar
  64. 64.
    Nudo RJ, Wise BM, SiFuentes F, Milliken GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272:1791–4.PubMedCrossRefGoogle Scholar
  65. 65.
    Jenkins WM, Merzenich MM. Reorganization of neocortical representations after brain injury: a neurophysiological model of the bases of recovery from stroke. Prog Brain Res. 1987;71:249–66.PubMedCrossRefGoogle Scholar
  66. 66.
    Soechting JF, Lacquaniti F. Invariant characteristics of a pointing movement in man. J Neurosci. 1981;1:710–20.PubMedGoogle Scholar
  67. 67.
    Flash T, Hogan N. The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci. 1985;5:1688–703.PubMedGoogle Scholar
  68. 68.
    Krebs HI, Aisen ML, Volpe BT, Hogan N. Quantization of continuous arm movements in humans with brain injury. Proc Natl Acad Sci U S A. 1999;96:4645–9.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Krebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe BT, Hogan N. Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robot. 2003;15:7–20.CrossRefGoogle Scholar
  70. 70.
    Michmizos KP, Krebs HI. Pointing with the ankle: the speed-accuracy trade-off. Exp Brain Res. 2014;232:647–57.PubMedCrossRefGoogle Scholar
  71. 71.
    Michmizos KP, Vaisman L, Krebs HI. A comparative analysis of speed profile models for ankle pointing movements: evidence that lower and upper extremity discrete movements are controlled by a single invariant strategy. Front Hum Neurosci. 2014;8:962.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Michmizos KP, Krebs HI. Reaction time in ankle movements: a diffusion model analysis. Exp Brain Res. 2014;232:3475–88.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    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;13:33–9.PubMedCrossRefGoogle Scholar
  74. 74.
    Lam T, Anderschitz M, Dietz V. Contribution of feedback and feedforward strategies to locomotor adaptations. J Neurophysiol. 2006;95:766–73.PubMedCrossRefGoogle Scholar
  75. 75.
    Fitts PM. The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol. 1954;47:381–91.PubMedCrossRefGoogle Scholar
  76. 76.
    Fitts PM, Peterson JR. Information capacity of discrete motor responses. J Exp Psychol. 1964;67:103–12.PubMedCrossRefGoogle Scholar
  77. 77.
    Krebs HI, Rossi S, Kim SJ, Artemiadis PK, Williams D, Castelli E, Cappa P. Pediatric anklebot. IEEE Int Conf Rehabil Robot. 2011;2011:5975410.PubMedGoogle Scholar
  78. 78.
    Krebs HI, Hogan N, Aisen ML, Volpe BT. Robot-aided neurorehabilitation. IEEE Trans Rehabil Eng. 1998;6:75–87.PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Krebs HI, Hogan N. Robotic therapy: the tipping point. Am J Phys Med Rehabil. 2012;91:S290–7.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Fasoli SE, Ladenheim B, Mast J, Krebs HI. New horizons for robot-assisted therapy in pediatrics. Am J Phys Med Rehabil. 2012;91:S280–9.PubMedCrossRefGoogle Scholar
  81. 81.
    Krebs HI, Fasoli SE, Dipietro L, Fragala-Pinkham M, Hughes R, Stein J, Hogan N. Motor learning characterizes habilitation of children with hemiplegic cerebral palsy. Neurorehabil Neural Repair. 2012;26:855–60.PubMedPubMedCentralCrossRefGoogle Scholar
  82. 82.
    Woodworth RS. Accuracy of voluntary movement. Psychol Rev. 1899;3:13.Google Scholar
  83. 83.
    Meyer DE, Abrams RA, Kornblum S, Wright CE, Smith JE. Optimality in human motor performance: ideal control of rapid aimed movements. Psychol Rev. 1988;95:340–70.PubMedCrossRefGoogle Scholar
  84. 84.
    Crossman ER, Goodeve PJ. Feedback control of hand-movement and Fitts’ law. Q J Exp Psychol A. 1983;35:251–78.PubMedCrossRefGoogle Scholar
  85. 85.
    Novak KE, Miller LE, Houk JC. The use of overlapping submovements in the control of rapid hand movements. Exp Brain Res. 2002;144:351–64.PubMedCrossRefGoogle Scholar
  86. 86.
    Elliott D, Helsen WF, Chua R. A century later: Woodworth’s (1899) two-component model of goal-directed aiming. Psychol Bull. 2001;127:342–57.PubMedCrossRefGoogle Scholar
  87. 87.
    Wu J, Yang J, Honda T. Fitts’ law holds for pointing movements under conditions of restricted visual feedback. Hum Mov Sci. 2010;29:882–92.PubMedCrossRefGoogle Scholar
  88. 88.
    Michmizos KP, Krebs HI. Serious games for the pediatric anklebot. In: Biomedical robotics and biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS international conference on. 2012, p. 1710–14.Google Scholar
  89. 89.
    Michmizos KP, Krebs HI. Assist-as-needed in lower extremity robotic therapy for children with cerebral palsy. In: Biomedical robotics and biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS international conference on. 2012, p. 1081–86.Google Scholar
  90. 90.
    Pretterklieber ML. Anatomy and kinematics of the human ankle joint. Radiologe. 1999;39:1–7.PubMedCrossRefGoogle Scholar
  91. 91.
    Charles SK, Hogan N. Dynamics of wrist rotations. J Biomech. 2011;44:614–21.PubMedCrossRefGoogle Scholar
  92. 92.
    Vaisman L, Dipietro L, Krebs HI. A comparative analysis of speed profile models for wrist pointing movements. IEEE Trans Neural Syst Rehabil Eng. 2013;21:756–66.PubMedCrossRefGoogle Scholar
  93. 93.
    Plamondon R, Alimi AM, Yergeau P, Leclerc F. Modelling velocity profiles of rapid movements: a comparative study. Biol Cybern. 1993;69:119–28.PubMedCrossRefGoogle Scholar
  94. 94.
    Stein RB, Cody FW, Capaday C. The trajectory of human wrist movements. J Neurophysiol. 1988;59:1814–30.PubMedGoogle Scholar
  95. 95.
    Plamondon R. A theory of rapid movements. In: Stelmach GE, Requin J, editors. Tutorials in motor behavior II. Amsterdam: Distributors for the U.S. and Canada, Elsevier Science Pub. Co; 1992. p. 55–69.Google Scholar
  96. 96.
    Mitnitski AB. Kinematic models cannot provide insight into motor control. Behav Brain Sci. 1997;20:318–19.Google Scholar
  97. 97.
    Plamondon R, Feng CH, Woch A. A kinematic theory of rapid human movement. Part IV: a formal mathematical proof and new insights. Biol Cybern. 2003;89:126–38.PubMedCrossRefGoogle Scholar
  98. 98.
    Donders FC. On the speed of mental processes. Acta Psychol (Amst). 1969;30:412–31.CrossRefGoogle Scholar
  99. 99.
    Hick WE. On the rate of gain of information. Q J Exp Psychol. 1952;4:11–26.CrossRefGoogle Scholar
  100. 100.
    Hyman R. Stimulus information as a determinant of reaction time. J Exp Psychol. 1953;45:188–96.PubMedCrossRefGoogle Scholar
  101. 101.
    Shannon CE, Weaver W. The mathematical theory of communication. Urbana: University of Illinois Press; 1949.Google Scholar
  102. 102.
    Laming DRJ. Information theory of choice-reaction times. London: Academic Press; 1968.Google Scholar
  103. 103.
    Boff KR, Kaufman L, Thomas JP. Handbook of perception and human performance. vol. 2. Cognitive processes and performance, DTIC document. 1994.Google Scholar
  104. 104.
    Miller JO, Low K. Motor processes in simple, go/no-go, and choice reaction time tasks: a psychophysiological analysis. J Exp Psychol Hum Percept Perform. 2001;27:266–89.PubMedCrossRefGoogle Scholar
  105. 105.
    Brown RG, Jahanshahi M, Marsden CD. Response choice in Parkinson’s disease. The effects of uncertainty and stimulus-response compatibility. Brain. 1993;116(Pt 4):869–85.PubMedCrossRefGoogle Scholar
  106. 106.
    Evarts EV, Teravainen H, Calne DB. Reaction time in Parkinson’s disease. Brain. 1981;104:167–86.PubMedCrossRefGoogle Scholar
  107. 107.
    Goodrich S, Henderson L, Kennard C. On the existence of an attention-demanding process peculiar to simple reaction time: converging evidence from Parkinson’s disease. Cogn Neuropsychol. 1989;6:309–31.CrossRefGoogle Scholar
  108. 108.
    Jahanshahi M, Brown RG, Marsden CD. A comparative study of simple and choice reaction time in Parkinson’s, Huntington’s and cerebellar disease. J Neurol Neurosurg Psychiatry. 1993;56:1169–77.PubMedPubMedCentralCrossRefGoogle Scholar
  109. 109.
    Marsden CD. The mysterious motor function of the basal ganglia: the Robert Wartenberg lecture. Neurology. 1982;32:514–39.PubMedCrossRefGoogle Scholar
  110. 110.
    Rogers MW, Chan CW. Motor planning is impaired in Parkinson’s disease. Brain Res. 1988;438:271–6.PubMedCrossRefGoogle Scholar
  111. 111.
    Gorus E, De Raedt R, Lambert M, Lemper JC, Mets T. Reaction times and performance variability in normal aging, mild cognitive impairment, and Alzheimer’s disease. J Geriatr Psychiatry Neurol. 2008;21:204–18.PubMedCrossRefGoogle Scholar
  112. 112.
    Martelli M, Barban F, Zoccolotti P, Silveri MC. Slowing of information processing in Alzheimer disease: motor as well as cognitive factors. Cogn Behav Neurol. 2012;25:175–85.PubMedCrossRefGoogle Scholar
  113. 113.
    Fernaeus SE, Ostberg P, Wahlund LO. Late reaction times identify MCI. Scand J Psychol. 2013;54:283–5.PubMedCrossRefGoogle Scholar
  114. 114.
    Chang JJ, Wu TI, Wu WL, Su FC. Kinematical measure for spastic reaching in children with cerebral palsy. Clin Biomech (Bristol, Avon). 2005;20:381–8.CrossRefGoogle Scholar
  115. 115.
    Horgan JS. Reaction-time and movement-time of children with cerebral palsy: under motivational reinforcement conditions. Am J Phys Med. 1980;59:22–9.PubMedGoogle Scholar
  116. 116.
    Schmitz N, Daly E, Murphy D. Frontal anatomy and reaction time in autism. Neurosci Lett. 2007;412:12–7.PubMedCrossRefGoogle Scholar
  117. 117.
    Leth-Steensen C, Elbaz ZK, Douglas VI. Mean response times, variability, and skew in the responding of ADHD children: a response time distributional approach. Acta Psychol (Amst). 2000;104:167–90.CrossRefGoogle Scholar
  118. 118.
    Zahn TP, Kruesi MJ, Rapoport JL. Reaction time indices of attention deficits in boys with disruptive behavior disorders. J Abnorm Child Psychol. 1991;19:233–52.PubMedCrossRefGoogle Scholar
  119. 119.
    King B, Wood C, Faulkner D. Sensitivity to visual and auditory stimuli in children with developmental dyslexia. Dyslexia. 2008;14:116–41.PubMedCrossRefGoogle Scholar
  120. 120.
    Fjell AM, Westlye LT, Amlien IK, Walhovd KB. Reduced white matter integrity is related to cognitive instability. J Neurosci. 2011;31:18060–72.PubMedCrossRefGoogle Scholar
  121. 121.
    Tamnes CK, Fjell AM, Westlye LT, Østby Y, Walhovd KB. Becoming consistent: developmental reductions in intraindividual variability in reaction time are related to white matter integrity. J Neurosci. 2012;32:972–82.PubMedCrossRefGoogle Scholar
  122. 122.
    Moy G, Millet P, Haller S, Baudois S, de Bilbao F, Weber K, Lovblad K, Lazeyras F, Giannakopoulos P, Delaloye C. Magnetic resonance imaging determinants of intraindividual variability in the elderly: combined analysis of grey and white matter. Neuroscience. 2011;186:88–93.PubMedCrossRefGoogle Scholar
  123. 123.
    Anstey KJ, Mack HA, Christensen H, Li S-C, Reglade-Meslin C, Maller J, Kumar R, Dear K, Easteal S, Sachdev P. Corpus callosum size, reaction time speed and variability in mild cognitive disorders and in a normative sample. Neuropsychologia. 2007;45:1911–20.PubMedCrossRefGoogle Scholar
  124. 124.
    MacDonald SW, Li SC, Backman L. Neural underpinnings of within-person variability in cognitive functioning. Psychol Aging. 2009;24:792–808.PubMedCrossRefGoogle Scholar
  125. 125.
    Mirabella G, Iaconelli S, Modugno N, Giannini G, Lena F, Cantore G. Stimulation of subthalamic nuclei restores a near normal planning strategy in Parkinson’s patients. PLoS One. 2013;8:e62793.PubMedPubMedCentralCrossRefGoogle Scholar
  126. 126.
    Baird BJ, Tombaugh TN, Francis M. The effects of practice on speed of information processing using the Adjusting-Paced Serial Addition Test (Adjusting-PSAT) and the Computerized Tests of Information Processing (CTIP). Appl Neuropsychol. 2007;14:88–100.PubMedCrossRefGoogle Scholar
  127. 127.
    Bisson E, Contant B, Sveistrup H, Lajoie Y. Functional balance and dual-task reaction times in older adults are improved by virtual reality and biofeedback training. Cyberpsychol Behav. 2007;10:16–23.PubMedCrossRefGoogle Scholar
  128. 128.
    Light KE, Reilly MA, Behrman AL, Spirduso WW. Reaction times and movement times: benefits of practice to younger and older adults. J Aging Phys Act. 1996;4:27–41.Google Scholar
  129. 129.
    Rikli RE, Edwards DJ. Effects of a three-year exercise program on motor function and cognitive processing speed in older women. Res Q Exerc Sport. 1991;62:61–7.PubMedCrossRefGoogle Scholar
  130. 130.
    Garry MI, Franks IM. Reaction time differences in spatially constrained bilateral and unilateral movements. Exp Brain Res. 2000;131:236–43.PubMedCrossRefGoogle Scholar
  131. 131.
    Garry MI, Franks IM. Spatially precise bilateral arm movements are controlled by the contralateral hemisphere: evidence from a lateralized visual stimulus paradigm. Exp Brain Res. 2002;142:292–6.PubMedCrossRefGoogle Scholar
  132. 132.
    Schieppati M, Trompetto C, Abbruzzese G. Selective facilitation of responses to cortical stimulation of proximal and distal arm muscles by precision tasks in man. J Physiol. 1996;491:551–62.PubMedPubMedCentralCrossRefGoogle Scholar
  133. 133.
    Lee H, Krebs HI, Hogan N. Multivariable dynamic ankle mechanical impedance with active muscles. IEEE Trans Neural Syst Rehabil Eng. 2014;22:971–81.PubMedPubMedCentralCrossRefGoogle Scholar
  134. 134.
    Hogan N. Impedance control: an approach to manipulation. ASME J Dyn Syst Meas Control. 1985;107:1–24.CrossRefGoogle Scholar
  135. 135.
    Franklin DW, Liaw G, Milner TE, Osu R, Burdet E, Kawato M. Endpoint stiffness of the arm is directionally tuned to instability in the environment. J Neurosci. 2007;27:7705–16.PubMedCrossRefGoogle Scholar
  136. 136.
    Toffin D, McIntyre J, Droulez J, Kemeny A, Berthoz A. Perception and reproduction of force direction in the horizontal plane. J Neurophysiol. 2003;90:3040–53.PubMedCrossRefGoogle Scholar
  137. 137.
    Hogan N, Buerger SP. Impedance and interaction control. In: Kurfess TR, editor. Robotics and automation handbook. Boca Raton: CRC Press; 2004. 19:1–24.Google Scholar
  138. 138.
    Ferris DP, Farley CT. Interaction of leg stiffness and surfaces stiffness during human hopping. J Appl Physiol (1985). 1997;82:15–22; discussion 13–4.Google Scholar
  139. 139.
    Ferris DP, Louie M, Farley CT. Running in the real world: adjusting leg stiffness for different surfaces. Proc Biol Sci. 1998;265:989–94.PubMedPubMedCentralCrossRefGoogle Scholar
  140. 140.
    Hansen AH, Childress DS, Miff SC, Gard SA, Mesplay KP. The human ankle during walking: implications for design of biomimetic ankle prostheses. J Biomech. 2004;37:1467–74.PubMedCrossRefGoogle Scholar
  141. 141.
    Lark SD, Buckley JG, Bennett S, Jones D, Sargeant AJ. Joint torques and dynamic joint stiffness in elderly and young men during stepping down. Clin Biomech (Bristol, Avon). 2003;18:848–55.CrossRefGoogle Scholar
  142. 142.
    Selles RW, Li X, Lin F, Chung SG, Roth EJ, Zhang LQ. Feedback-controlled and programmed stretching of the ankle plantarflexors and dorsiflexors in stroke: effects of a 4-week intervention program. Arch Phys Med Rehabil. 2005;86:2330–6.PubMedCrossRefGoogle Scholar
  143. 143.
    Lee H, Krebs HI, Hogan N. Multivariable dynamic ankle mechanical impedance with relaxed muscles. IEEE Trans Neural Syst Rehabil Eng. 2014;22:1104–14.PubMedPubMedCentralCrossRefGoogle Scholar
  144. 144.
    Lee H, Hogan N. Time-varying ankle mechanical impedance during human locomotion. IEEE Trans Neural Syst Rehabil Eng. 2015;23:755–64.PubMedCrossRefGoogle Scholar
  145. 145.
    Wise RA, Brown CD. Minimal clinically important differences in the six-minute walk test and the incremental shuttle walking test. COPD. 2005;2:125–9.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing 2016

Authors and Affiliations

  • Hermano Igo Krebs
    • 1
    • 2
    • 3
    • 4
    • 5
  • Konstantinos Michmizos
    • 6
  • Tyler Susko
    • 7
  • Hyunglae Lee
    • 8
  • Anindo Roy
    • 2
  • Neville Hogan
    • 9
  1. 1.Department of Mechanical EngineeringMIT-Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of NeurologyUniversity of Maryland, School of MedicineBaltimoreUSA
  3. 3.Institute of NeuroscienceUniversity of NewcastleNewcastle Upon TyneUK
  4. 4.Department of Rehabilitation Medicine IFujita Health University, School of MedicineNagoyaJapan
  5. 5.Department of Mechanical Science and BioengineeringOsaka UniversityOsakaJapan
  6. 6.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  7. 7.Department of Mechanical EngineeringUniversity of California Santa BarbaraSanta BarbaraUSA
  8. 8.School for Engineering of Matter, Transport, and EnergyArizona State UniversityTempeUSA
  9. 9.Department of Mechanical Engineering, Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA

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