Abstract
Neuro-rehabilitation is currently undergoing a technological revolution! Groups of engineers and rehabilitation specialists are working on designing and testing a great variety of rehabilitation devices and systems. The reason for this is that, although it is generally accepted that rehabilitation improves outcome after stroke, patients are still left with impairments causing various levels of handicap and limiting their integration in community life. Comparison of traditional rehabilitation techniques has failed to show superiority of one over another (15), and concepts for rehabilitation have been changing over the last 20 years, with the biggest change being evaluation. There is a move to make rehabilitation techniques more evidence based. As such, numerous research teams have set about to create more effective rehabilitation techniques based on principles of motor control and learning and incorporating new technology to fulfil the principal goals of rehabilitation: increased functional ability and increased participation in the community. The aim of this chapter is to discuss applications for augmented feedback (AF) in rehabilitation of motor skills of patients with neurological disorders, in particular within virtual reality (VR) environments associated or not with mechatronic devices (robotics). First, we will examine some motor learning principles relevant to rehabilitation and how AF fits into these concepts. We will then go on to review applications of AF used for rehabilitation of specific movement parameters. We will also discuss the use of feedback distortion to manipulate action-perception coupling and systems based on movement observation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adamovich SV, et al. (2005) A virtual reality-based exercise system for hand rehabilitation post-stroke. Presence 14(2):161–174
Amirabdollahian F, et al. (2007) Multivariate analysis of the Fugl-Meyer outcome measures assessing the effectiveness of GENTLE/S robot-mediated stroke therapy. J Neuroeng Rehabil 4:4
Bach-y-Rita P, et al. (2002) Computer-assisted motivating rehabilitation (CAMR) for institutional, home, and educational late stroke programs. Top Stroke Rehabil 8(4):1–10
Batavia M, et al. (2001) A do-it-yourself membrane-activated auditory feedback device for weight bearing and gait training: a case report. Arch Phys Med Rehabil 82(4):541–545
Bernstein N (1967) The coordination and regulation of movements. Pergamon Press, Oxford, England
Bi S, Ji L, Wang Z (2005) Robot-aided sensorimotor arm training methods based on neurological rehabilitation principles in stroke and brain injury patients. In: 27th Annual International Conference of the IEEE Engineering in Medecine and Biology Society. Shanghai, China, pp. 5025–5027
Brewer BR, Klatzky R, Matsuoka Y (2008) Visual feedback distortion in a robotic environment for hand rehabilitation. Brain Res Bull 75(6):804–813
Broeren J, et al. (2007) Assessment and training in a 3-dimensional virtual environment with haptics: a report on 5 cases of motor rehabilitation in the chronic stage after stroke. Neurorehabil Neural Repair 21(2):180–189
Carey JR, et al. (2007) Comparison of finger tracking versus simple movement training via telerehabilitation to alter hand function and cortical reorganization after stroke. Neurorehabil Neural Repair 21(3):216–232
Carignan CR, Krebs HI (2006) Telerehabilitation robotics: bright lights, big future? J Rehabil Res Dev 43(5):695–710
Carr R, Shepherd J (1998) Neurological rehabilitation. Optimizing motor performance. Butterworth-Heinemann, Oxford
Cassadio M, Morasso P, Sanguineti V, Giannoni P (2006) Impedance-controlled, minimally-assistive robotic training of severely impaired hemiparetic patients. In: BioRob 2006. Piza, Italy
Colombo R, et al. (2007) Design strategies to improve patient motivation during robotaided rehabilitation. J Neuroeng Rehabil 4:3
Eng K, et al. (2007) Interactive visuo-motor therapy system for stroke rehabilitation. Med Biol Eng Comput 45(9):901–907
Ernst E (1990) A review of stroke rehabilitation and physiotherapy. Stroke 21(7):1081–1085
Feintuch U, et al. (2006) Integrating haptic-tactile feedback into a video-capture-based virtual environment for rehabilitation. Cyberpsychol Behav 9(2):129–132
Flanagan JR, Rao AK (1995) Trajectory adaptation to a nonlinear visuomotor transformation: evidence of motion planning in visually perceived space. J Neurophysiol 74(5):2174–2178
Fung J, et al. (2006) A treadmill and motion coupled virtual reality system for gait training post-stroke. Cyberpsychol Behav 9(2):157–162
Giraux P, Sirigu A (2003) Illusory movements of the paralyzed limb restore motor cortex activity. Neuroimage 20(Suppl 1):S107–S111
Hanlon, R.E., Motor learning following unilateral stroke. Arch Phys Med Rehabil, 1996. 77(8): p. 811–5
Hoffman AN, et al. (2008) Environmental enrichment-mediated functional improvement after experimental traumatic brain injury is contingent on task-specific neurobehavioral experience. Neurosci Lett 431(3):226–230
Hogan N, Krebs HI (2004) Interactive robots for neuro-rehabilitation. Restor Neurol Neurosci 22(3–5):349–358
Holden MK, et al. (2001) Retraining movemsent in patients with acquired brain injury using a virtual environment. Stud Health Technol Inform 81:192–198
Holden MK (2005) Virtual environments for motor rehabilitation: review. Cyberpsychol Behav 8(3):187–211; discussion 212–219
Huang H (2005) Interactive multimodal biofeedback for task-orientated neural rehabilitation. In: 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Shanghai, China, pp. 2547–2550
Huijgen BC, et al. (2008) Feasibility of a home-based telerehabilitation system compared to usual care: arm/hand function in patients with stroke, traumatic brain injury and multiple sclerosis. J Telemed Telecare 14(5):249–256
Johansson RS (1991) How is grasping modified by somatosensory input? In: Humphrey DR, Freund H-J (eds) Motor control: concepts and issues, pp. 331–355. Wiley, New York
Johnson MJ, et al. (2007) Potential of a suite of robot/computer-assisted motivating systems for personalized, home-based, stroke rehabilitation. J Neuroeng Rehabil 4:6
Koenig A, et al. (2008) Virtual gait training for children with cerebral palsy using the Lokomat gait orthosis. Stud Health Technol Inform 132:204–209
Kwakkel G (2006) Impact of intensity of practice after stroke: issues for consideration. Disabil Rehabil 28(13–14):823–830
Lamontagne A, et al. (2007) Modulation of walking speed by changing optic flow in persons with stroke. J Neuroeng Rehabil 4:22
Levin MF (1996) Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. Brain 119:281–293
Lewis-Brooks A (2004) HUMANICS 1 — a feasibility study to create a home internet based telehealth product to supplement acquired brain injury therapy. In: Proceedings of the 5th International Conference on Disability, Virtual Reality and Associated Technologies, Oxford, UK
Liu J, Cramer SC, Reinkensmeyer DJ (2006) Learning to perform a new movement with robotic assistance: comparison of haptic guidance and visual demonstration. J Neuroeng Rehabil 3:20
Lövquist E, Dreifaldt U (2006) The design of a haptic exercise for post-stroke arm rehabilitation. In: Proceedings of the 6th International Conference on Disability, Virtual Reality and Associated Technologies. Esbjerg, Denmark, pp. 309–315
Lum PS, et al. (2002) Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Arch Phys Med Rehabil 83(7):952–959
Lunenburger L, et al. (2004) Biofeedback in gait training with the robotic orthosis Lokomat. In: Conference Proceedings — IEEE Engineering in Medicine and Biology Society, San Francisco, USA. pp. 4888–4891
Luo X, et al. (2005.) Integration of augmented reality and assistive devices for poststroke hand opening rehabilitation. In: 27th Annual International Conference of the IEEE Engineering in Medecine and Biology society. Shanghai, China, pp. 6855–6858
Mali U, Goljar N, Munih M (2006) Application of haptic interface for finger exercise. IEEE Trans Neural Syst Rehabil Eng 14(3):352–360
Maulucci RA, Eckhouse RH (2001) Retraining reaching in chronic stroke with real-time auditory feedback. NeuroRehabilitation 16(3):171–82
Michaelsen SM, Dannenbaum R, Levin MF (2006) Task-specific training with trunk restraint on arm recovery in stroke: randomized control trial. Stroke 37(1):186–192
Mirelman A, Bonato P, Deutsch JE (2008) Effects of training with a robot-virtual reality system compared with a robot alone on the gait of individuals after stroke. Stroke 40(1):167–174
Moreland JD, et al. (2003) Progressive resistance strengthening exercises after stroke: a single-blind randomized controlled trial. Arch Phys Med Rehabil 84(10):1433–1440
Mulder T (2007) Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm 114(10):1265–1278
Nudo RJ (2006) Mechanisms for recovery of motor function following cortical damage. Curr Opin Neurobiol 16(6):638–644
Nugent JA, Schurr KA, Adams RD (1994) A dose-response relationship between amount of weight-bearing exercise and walking outcome following cerebrovascular accident. Arch Phys Med Rehabil 75(4):399–402
Page SJ (2003) Intensity versus task-specificity after stroke: how important is intensity? Am J Phys Med Rehabil 82(9):730–732
Page SJ, Gater DR, Bach YRP (2004) Reconsidering the motor recovery plateau in stroke rehabilitation. Arch Phys Med Rehabil 85(8):1377–1381
Patton J, et al., (2004) Robotics and virtual reality: the development of a life-sized 3-D system for the reabilitation of motor function. In: Conference Proceetings — IEEE Engineering in Medecine and Biology Society, San Francisco, USA pp. 4840–4843
Patton JL, et al. (2006) Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res 168(3):368–383
Perez MA, Lungholt BK, Nielsen JB (2005) Presynaptic control of group Ia afferents in relation to acquisition of a visuo-motor skill in healthy humans. J Physiol 568(Pt 1):343–354
Piron L, et al. (2008) Satisfaction with care in post-stroke patients undergoing a telerehabilitation programme at home. J Telemed Telecare, 14(5):257–260
Platz T, et al. (2005) Impairment-oriented training or Bobath therapy for severe arm paresis after stroke: a single-blind, multicentre randomized controlled trial. Clin Rehabil 19(7):714–724
Regnaux JP, et al. (2008) Effects of loading the unaffected limb for one session of locomotor training on laboratory measures of gait in stroke. Clin Biomech (Bristol, Avon) 23(6):762–768
Reinkensmeyer DJ, et al. (2002) Web-based telerehabilitation for the upper extremity after stroke. IEEE Trans Neural Syst Rehabil Eng 10(2):102–108
Rizzolatti G, et al. (1996) Premotor cortex and the recognition of motor actions. Brain Res Cogn Brain Res 3(2):131–141
Roby-Brami A, et al. (2003) Motor compensation and recovery for reaching in stroke patients. Acta Neurol Scand 107(5):369–381
Sanchez RJ, et al. (2006) Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Trans Neural Syst Rehabil Eng 14(3):378–89
Schauer M, Mauritz KH (2003) Musical motor feedback (MMF) in walking hemiparetic stroke patients: randomized trials of gait improvement. Clin Rehabil 17(7):713–722
Schmidt R, Wrisberg C (2004) Motor learning and performance. Human Kinetics, Leeds, England
Sribunruangrit N, et al. (2004) Speed-accuracy tradeoff during performance of a tracking task without visual feedback IEEE Trans Neural Syst Rehabil Eng 12(1):131–139
Stein J, et al. (2004) Comparison of two techniques of robot-aided upper limb exercise training after stroke. Am J Phys Med Rehabil 83(9):720–728
Takahashi CD, et al. (2008) Robot-based hand motor therapy after stroke. Brain 131(Pt 2):425–437
Taub E, Uswatte G, Elbert T (2002) New treatments in neurorehabilitation founded on basic research. Nat Rev Neurosci 3(3):228–236
Taub E, et al. (2005) AutoCITE: automated delivery of CI therapy with reduced effort by therapists. Stroke 36(6):1301–1304
Taub E, Uswatt G (2006) Constraint-induced movement therapy: answers and questions after two decades of research. NeuroRehabilitation 21(2):93–95
Todorov E, Shadmehr R, Bizzi E (1997) Augmented feedback presented in a virtual environment accelerates learning of a difficult motor task. J Mot Behav 29(2):147–158
van Vliet PM, Wulf G (2006) Extrinsic feedback for motor learning after stroke: what is the evidence? Disabil Rehabil 28(13–14):831–840
Viau A, et al. (2004) Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis. J Neuroeng Rehabil 1(1):11
Volpe BT, et al. (2008) Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke. Neurorehabil Neural Repair 22(3):305–310
Winstein CJ, Merians AS, Sullivan KJ (1999) Motor learning after unilateral brain damage. Neuropsychologia 37(8):975–987
Woldag H, Hummelsheim H (2002) Evidence-based physiotherapeutic concepts for improving arm and hand function in stroke patients: a review. J Neurol 249(5):518–528
Wu C, et al. (2000) A kinematic study of contextual effects on reaching performance in persons with and without stroke: influences of object availability. Arch Phys Med Rehabil 81(1):95–101
Yavuzer G, et al. (2008) Mirror therapy improves hand function in subacute stroke: a randomized controlled trial. Arch Phys Med Rehabil 89(3):393–398
Yang, Y.R., et al., Virtual reality-based training improves community ambulation in individuals with stroke: a randomized controlled trial. Gait Posture, 2008. 28(2):201–206
Zheng H, et al. (2006) SMART project: application of emerging information and communication technology to home-based rehabilitation for stroke patients. In: Proceedings of the 6th International Conference on Disability, Virtual Reality and Associated Technologies. University of Reading, UK: Esbjerg, Denmark, pp. 215–220
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag France, Paris
About this chapter
Cite this chapter
Robertson, J.V.G., Roby-Brami, A. (2010). Augmented feedback, virtual reality and robotics for designing new rehabilitation methods. In: Rethinking physical and rehabilitation medicine. Collection de L’Académie Européenne de Médecine de Réadaptation. Springer, Paris. https://doi.org/10.1007/978-2-8178-0034-9_12
Download citation
DOI: https://doi.org/10.1007/978-2-8178-0034-9_12
Publisher Name: Springer, Paris
Print ISBN: 978-2-8178-0033-2
Online ISBN: 978-2-8178-0034-9
eBook Packages: MedicineMedicine (R0)