Skip to main content
SpringerLink
Log in

Basis and Clinical Evidence of Virtual Reality-Based Rehabilitation of Sensorimotor Impairments After Stroke

  • Chapter
  • First Online:
Neurorehabilitation Technology

  • 1168 Accesses

Abstract

In the recent years, the use of virtual reality (VR) to enhance motor skills of persons with activity and participation restriction due to disease or injury has become an important area of research and translation to practice. In this chapter, we describe the design of such VR systems and their underlying principles, such as experience-dependent neuroplasticity and motor learning. Further, psychological constructs related to motivation, including salience, goal setting, and rewards are commonly utilized in VR to optimize motivation during rehabilitation activities. Hence, virtually simulated activities are considered to be ideal for [1] the delivery of specific feedback, [2] the ability to perform large volumes of training, and [3] the presentation of precisely calibrated difficulty levels, which maintain a high level of challenge throughout long training sessions. These underlying principles are contrasted with a growing body of research comparing the efficacy of VR with traditionally presented rehabilitation activities in persons with stroke that demonstrate comparable or better outcomes for VR. In addition, a small body of literature has utilized direct assays of neuroplasticity to evaluate the effects of virtual rehabilitation interventions in persons with stroke. Promising developments and findings also arise from the use of off-the-shelf video game systems for virtual rehabilitation purposes and the integration of VR with robots and brain-computer interfaces. Several challenges limiting the translation of virtual rehabilitation into routine rehabilitation practice need to be addressed but the field continues to hold promise to answer key issues faced by modern healthcare.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Burdea GC, Coiffet P. Virtual reality technology. Presence. 2003;12(6):663–4.

    Google Scholar 

  2. Wilson PN, Foreman N, Stanton D. Virtual reality, disability and rehabilitation. Disabil Rehabil. 1997;19(6):213–20.

    CAS  PubMed  Google Scholar 

  3. Adamovich SV, et al. Sensorimotor training in virtual reality: a review. NeuroRehabil. 2009;25(1):29–44.

    Google Scholar 

  4. Slater M. Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos Trans R Soc B Biol Sci. 2009;364(1535):3549–57.

    Google Scholar 

  5. Slater M, Wilbur S. A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence. 1997;6(6):603–16.

    Google Scholar 

  6. Baños RM, et al. Immersion and emotion: their impact on the sense of presence. Cyberpsychol Behav. 2004;7(6):734–41.

    PubMed  Google Scholar 

  7. Llorens R, et al. Tracking systems for virtual rehabilitation: objective performance vs subjective experience: a practical scenario. Sensors. 2015;15(3):6586–606.

    PubMed  PubMed Central  Google Scholar 

  8. Stanney KM, Mourant RR, Kennedy RS. Human factors issues in virtual environments: a review of the literature. Presence. 1998;7(4):327–51.

    Google Scholar 

  9. Arzy S, et al. Neural mechanisms of embodiment: asomatognosia due to premotor cortex damage. Arch Neurol. 2006;63(7):1022–5.

    PubMed  Google Scholar 

  10. Legrand D. The bodily self: the sensori-motor roots of pre-reflective self-consciousness. Phenom Cog Sci. 2006;5(1):89–118.

    Google Scholar 

  11. Berlucchi G, Aglioti S. The body in the brain: neural bases of corporeal awareness. Trends Neurosci. 1997;20(12):560–4.

    CAS  PubMed  Google Scholar 

  12. Schultze U. Embodiment and presence in virtual worlds: a review. J Inf technol. 2010;25(4):434–49.

    Google Scholar 

  13. Grassini S, Laumann K. Questionnaire measures and physiological correlates of presence: a systematic review. Front Psychol. 2020;11:349.

    PubMed  PubMed Central  Google Scholar 

  14. Borrego A, et al. Embodiment and presence in virtual reality after stroke. a comparative study with healthy subjects. Front Neurol, 2019. 10(1061).

    Google Scholar 

  15. Schwind V, et al. Using presence questionnaires in virtual reality. In: Proceedings of the 2019 CHI conference on human factors in computing systems. 2019.

    Google Scholar 

  16. Longo MR, et al. What is embodiment? A psychometric approach. Cognition. 2008;107(3):978–98.

    PubMed  Google Scholar 

  17. Tsakiris M. My body in the brain: A neurocognitive model of body-ownership. Neuropsychologia. 2010;48(3):703–12.

    PubMed  Google Scholar 

  18. Botvinick M, Cohen J. Rubber hands /`feel/’ touch that eyes see. Nature. 1998;391(6669):756.

    CAS  PubMed  Google Scholar 

  19. Petkova VI, Ehrsson HH. If I were you: perceptual illusion of body swapping. PLoS ONE. 2008;3(12): e3832.

    PubMed  PubMed Central  Google Scholar 

  20. Petkova VI, Khoshnevis M, Ehrsson HH. The perspective matters! Multisensory integration in ego-centric reference frames determines full body ownership. Front Psychol, 2011. 2.

    Google Scholar 

  21. Slater M, et al. Inducing illusory ownership of a virtual body. Front Neurosci. 2009;3(2):214–20.

    PubMed  PubMed Central  Google Scholar 

  22. Banakou D, Groten R, Slater M. Illusory ownership of a virtual child body causes overestimation of object sizes and implicit attitude changes. Proc Natl Acad Sci U S A. 2013;110(31):12846–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Yee N, Bailenson J. The proteus effect: the effect of transformed self-representation on behavior. Human Commun Res. 2007;33(3):271–90.

    Google Scholar 

  24. Rand D, et al. Comparison of Two VR platforms for rehabilitation: video capture versus HMD. Presence. 2005;14(2):147–60.

    Google Scholar 

  25. Rand D, Kizony R, Weiss PT. The sony playstation II eyetoy: low-cost virtual reality for use in rehabilitation. J Neurol Phys Ther. 2008;32(4):155–63.

    PubMed  Google Scholar 

  26. Perez-Marcos D, Sanchez-Vives M, Slater M. Is my hand connected to my body? The impact of body continuity and arm alignment on the virtual hand illusion. Cog Neurodyn. 2012;6(4):295–305.

    Google Scholar 

  27. Weech S, Kenny S, Barnett-Cowan M. Presence and cybersickness in virtual reality are negatively related: a review. Front Psychol. 2019;10:158.

    PubMed  PubMed Central  Google Scholar 

  28. Kim J, Luu W, Palmisano S. Multisensory integration and the experience of scene instability, presence and cybersickness in virtual environments. Comput Hum Behav. 2020;113: 106484.

    Google Scholar 

  29. Specht J, et al. Acceptance of immersive head-mounted display virtual reality in stroke patients. Comput Human Behavior Reports. 2021;4: 100141.

    Google Scholar 

  30. Høeg ER, et al. System immersion in virtual reality-based rehabilitation of motor function in older adults: a systematic review and meta-analysis. Front Virtual Reality. 2021;2:30.

    Google Scholar 

  31. Schmidt RA. Motor learning principles for physical therapy. in Contemporary management of motor control problems: Proceedings of the II STEP Conference. Alexandria, VA: Foundation for Physical Therapy. 1991.

    Google Scholar 

  32. Wulf G, Shea C, Lewthwaite R. Motor skill learning and performance: a review of influential factors. Med Educ. 2010;44(1):75–84.

    PubMed  Google Scholar 

  33. Deutsch JE, et al. 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 

  34. Holden MK. Virtual environments for motor rehabilitation: Review. Cyberpsychol Behav. 2005;8(3):187–211.

    PubMed  Google Scholar 

  35. Deutsch JE, et al. Nintendo wii sports and wii fit game analysis, validation, and application to stroke rehabilitation. Top Stroke Rehabil. 2011;18(6):701–19.

    PubMed  Google Scholar 

  36. Fluet GG, Deutsch JE. Virtual reality for sensorimotor rehabilitation post-stroke: the promise and current state of the field. Curr Phys Med Rehabil Rep. 2013;1(1):9–20.

    PubMed  PubMed Central  Google Scholar 

  37. Levin MF, Weiss PL, Keshner EA. Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles. Phys Ther. 2015;95(3):415–25.

    PubMed  Google Scholar 

  38. Laver KE, et al., Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev, 2015. 2.

    Google Scholar 

  39. Levac, D. and H. Sveistrup, Motor Learning and Virtual Reality, in Virtual Reality for Physical and Motor Rehabilitation, P.L. Weiss, E.A. Keshner, and M.F. Levin, Editors. 2014, Springer: New York.

    Google Scholar 

  40. Kleim JA, Barbay S, Nudo RJ. Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol. 1998;80(6):3321–5.

    CAS  PubMed  Google Scholar 

  41. Janssen H, et al. An enriched environment increases activity in stroke patients undergoing rehabilitation in a mixed rehabilitation unit: a pilot non-randomized controlled trial. Disabil Rehabil. 2014;36(3):255–62.

    PubMed  Google Scholar 

  42. Sigrist R, et al. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychon Bull Rev. 2013;20(1):21–53.

    PubMed  Google Scholar 

  43. Winstein CJ. Knowledge of results and motor learning–implications for physical therapy. Phys Ther. 1991;71(2):140–9.

    CAS  PubMed  Google Scholar 

  44. Klinger E, Cherni H, Joseph P-A. Impact of contextual additional stimuli on the performance in a virtual activity of daily living (vADL) among patients with brain injury and controls. Int J Disabil Human Dev. 2014;13(3):377–82.

    Google Scholar 

  45. Molier BI, et al. Nature, timing, frequency and type of augmented feedback; does it influence motor relearning of the hemiparetic arm after stroke? A systematic review.. Disabil Rehabil. 2010;32(22):1799–809.

    PubMed  Google Scholar 

  46. Mirelman A, Bonato P, Deutsch JE. Effects of training with a robot-virtual reality system compared with a robot alone on the gait of individuals after stroke. Stroke. 2009;40(1):169–74.

    PubMed  Google Scholar 

  47. Subramanian SK, et al. Arm motor recovery using a virtual reality intervention in chronic stroke: randomized control trial. Neurorehabil Neural Repair. 2013;27(1):13–23.

    PubMed  Google Scholar 

  48. Holden M, et al. Virtual environment training improves motor performance in two patients with stroke: case report. J Neurol Phys Ther. 1999;23(2):57–67.

    Google Scholar 

  49. Schmidt, R.A., Motor learning and performance: a situation-based learning approach. 2008: Human Kinetics.

    Google Scholar 

  50. Magill, R.A., Motor Learning and Control: Current Concepts and Applications. 9th ed. 2007: McGraw Hill. 466.

    Google Scholar 

  51. Lam P, et al. A haptic-robotic platform for upper-limb reaching stroke therapy: Preliminary design and evaluation results. J NeuroEng Rehabil. 2008;5(1):15.

    PubMed  PubMed Central  Google Scholar 

  52. Colomer C, et al. Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke. J Neuroeng Rehabil. 2016;13(1):45.

    PubMed  PubMed Central  Google Scholar 

  53. Pichiorri F, et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015;77(5):851–65.

    PubMed  Google Scholar 

  54. Kizony R, et al. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Phys Ther. 2010;90(2):252–60.

    PubMed  Google Scholar 

  55. Adams RJ, et al. Assessing upper extremity motor function in practice of virtual activities of daily living. IEEE Trans Neural Syst Rehabil Eng. 2015;23(2):287–96.

    PubMed  Google Scholar 

  56. Cameirão MS, et al. Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. J Neuroeng Rehabil. 2010;7:48.

    PubMed  PubMed Central  Google Scholar 

  57. Shin J-H, Ryu H, Jang SH. A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. J Neuroreng Rehabil. 2014;11(1):32.

    Google Scholar 

  58. Levac DE, Huber ME, Sternad D. Learning and transfer of complex motor skills in virtual reality: a perspective review. J Neuroeng Rehabil. 2019;16(1):121.

    PubMed  PubMed Central  Google Scholar 

  59. Yang YR, et al. Virtual reality-based training improves community ambulation in individuals with stroke: a randomized controlled trial. Gait Posture. 2008;28(2):201–6.

    PubMed  Google Scholar 

  60. Fung J, et al. A treadmill and motion coupled virtual reality system for gait training post-stroke. Cyberpsychol Behav. 2006;9(2):157–62.

    PubMed  Google Scholar 

  61. Fung J, Perez CF. Sensorimotor enhancement with a mixed reality system for balance and mobility rehabilitation. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011. IEEE.

    Google Scholar 

  62. Jaffe DL, et al. Stepping over obstacles to improve walking in individuals with poststroke hemiplegia. J Rehabil Res Dev. 2004;41(3A):283–92.

    PubMed  Google Scholar 

  63. You SH, et al. Virtual reality–induced cortical reorganization and associated locomotor recovery in chronic stroke an experimenter-blind randomized study. Stroke. 2005;36(6):1166–71.

    PubMed  Google Scholar 

  64. Kwakkel G, et al. Effects of augmented exercise therapy time after stroke: a meta-analysis. Stroke. 2004;35(11):2529–39.

    PubMed  Google Scholar 

  65. Schmidt, R.A. and T. Lee, Motor control and learning. 2011: Human Kinetics Ltd; 5th Revised edition edition (1 Mar. 2011).

    Google Scholar 

  66. Kleim JA, Jones TA. Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J Speech Lang Hear Res. 2008;51(1):S225–39.

    PubMed  Google Scholar 

  67. Rand D, et al. Eliciting upper extremity purposeful movements using video games: a comparison with traditional therapy for stroke rehabilitation. Neurorehabil Neural Repair. 2014;28(8):733–9.

    PubMed  Google Scholar 

  68. Remple MS, et al. Sensitivity of cortical movement representations to motor experience: evidence that skill learning but not strength training induces cortical reorganization. Behav Brain Res. 2001;123(2):133–41.

    CAS  PubMed  Google Scholar 

  69. Dancause N, Nudo RJ. Shaping plasticity to enhance recovery after injury. Prog Brain Res. 2011;192:273–95.

    PubMed  PubMed Central  Google Scholar 

  70. Llorens R, et al. Effectiveness, usability, and cost-benefit of a virtual reality-based telerehabilitation program for balance recovery after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96(3):418–25.

    PubMed  Google Scholar 

  71. Cameirão M, et al. Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: A randomized controlled pilot study in the acute phase of stroke using the Rehabilitation Gaming System. Restor Neurol Neurosci. 2011;29(5):287–98.

    Google Scholar 

  72. Adamovich SV, et al. Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity. IEEE Trans Neural Syst Rehabil Eng. 2009;17(5):512–20.

    PubMed  PubMed Central  Google Scholar 

  73. Fluet GG, et al. Robots integrated with virtual reality simulations for customized motor training in a person with upper extremity hemiparesis: a case report. J Neurol Phys Ther. 2012;36(2):79–86.

    PubMed  PubMed Central  Google Scholar 

  74. Homberg V. Evidence based medicine in neurological rehabilitation–a critical review. Acta Neurochir Suppl. 2005;93:3–14.

    CAS  PubMed  Google Scholar 

  75. Ryan RM, Deci EL. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemp Educ Psychol. 2000;25(1):54–67.

    CAS  PubMed  Google Scholar 

  76. Swanson LR, Whittinghill DM. Intrinsic or extrinsic? using videogames to motivate stroke survivors: a systematic review. Games Health J. 2015;4(3):253–8.

    PubMed  Google Scholar 

  77. Boyle EA, et al. An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Comput Educ. 2016;94:178–92.

    Google Scholar 

  78. Beristain-Colorado MDP, et al. Standardizing the development of serious games for physical rehabilitation: conceptual framework proposal. JMIR Serious Games. 2021;9(2): e25854.

    PubMed  PubMed Central  Google Scholar 

  79. Barrett N, et al. The use and effect of video game design theory in the creation of game-based systems for upper limb stroke rehabilitation. J Rehabilitation Assistive Technol Eng. 2016;3:2055668316643644.

    CAS  Google Scholar 

  80. Linehan, C., et al. Practical, appropriate, empirically-validated guidelines for designing educational games. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011. ACM.

    Google Scholar 

  81. Faria AL, et al. Combined cognitive-motor rehabilitation in virtual reality improves motor outcomes in chronic stroke–a pilot study. Front Psychol. 2018;9:854.

    PubMed  PubMed Central  Google Scholar 

  82. Kannan L, et al. Cognitive-motor exergaming for reducing fall risk in people with chronic stroke: a randomized controlled trial. NeuroRehabilitation. 2019;44(4):493–510.

    PubMed  Google Scholar 

  83. House G, et al. Integrative rehabilitation of residents chronic post-stroke in skilled nursing facilities: the design and evaluation of the BrightArm Duo. Disabil Rehabil Assist Technol. 2016;11(8):683–94.

    CAS  PubMed  Google Scholar 

  84. Parker J, et al. The provision of feedback through computer-based technology to promote self-managed post-stroke rehabilitation in the home. Disabil Rehabil Assist Technol. 2014;9(6):529–38.

    PubMed  Google Scholar 

  85. Cirstea CM, Ptito A, Levin MF. Feedback and cognition in arm motor skill reacquisition after stroke. Stroke. 2006;37(5):1237–42.

    CAS  PubMed  Google Scholar 

  86. Kilduski NC, Rice MS. Qualitative and quantitative knowledge of results: effects on motor learning. Am J Occup Ther. 2003;57(3):329–36.

    PubMed  Google Scholar 

  87. Rosati G, et al. On the role of auditory feedback in robot-assisted movement training after stroke: review of the literature. Comp Int Neurosci. 2013;2013:11.

    Google Scholar 

  88. Lauber B, Keller M. Improving motor performance: Selected aspects of augmented feedback in exercise and health. Eur J Sport Sci. 2014;14(1):36–43.

    PubMed  Google Scholar 

  89. Cameron J, Banko KM, Pierce WD. Pervasive negative effects of rewards on intrinsic motivation: The myth continues. Behav Analyst. 2001;24(1):1.

    CAS  Google Scholar 

  90. Paulino, T., A.L. Faria, and S.B. i Badia. Reh@ City v2. 0: a comprehensive virtual reality cognitive training system based on personalized and adaptive simulations of activities of daily living. in 2019 5th Experiment International Conference (exp. at'19). 2019. IEEE.

    Google Scholar 

  91. Vourvopoulos, A., A. Ferreira, and S.B. i Badia. NeuRow: an immersive VR environment for motor-imagery training with the use of brain-computer interfaces and vibrotactile feedback. in International Conference on Physiological Computing Systems. 2016. SCITEPRESS.

    Google Scholar 

  92. Serradilla, J., et al. Automatic assessment of upper limb function during play of the action video game, circus challenge: validity and sensitivity to change. in 2014 IEEE 3nd International Conference on Serious Games and Applications for Health (SeGAH). 2014. IEEE.

    Google Scholar 

  93. Neil A, et al. Sony PlayStation EyeToy elicits higher levels of movement than the Nintendo Wii: implications for stroke rehabilitation. Eur J Phys Rehabil Med. 2012;49(1):13–21.

    PubMed  Google Scholar 

  94. Burke JW, et al. Optimising engagement for stroke rehabilitation using serious games. Vis Comput. 2009;25(12):1085–99.

    Google Scholar 

  95. Koritnik T, et al. Comparison of visual and haptic feedback during training of lower extremities. Gait Posture. 2010;32(4):540–6.

    CAS  PubMed  Google Scholar 

  96. Lee, M.H., et al. Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy. in 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 2020. IEEE.

    Google Scholar 

  97. Malone TW, Lepper MR. Making learning fun: A taxonomy of intrinsic motivations for learning. Aptitude, learning, and instruction. 1987;3:223–53.

    Google Scholar 

  98. Yerkes RM, Dodson JD. The relation of strength of stimulus to rapidity of habit-formation. J Comp Neurol and Psychol. 1908;18(5):459–82.

    Google Scholar 

  99. Duffy E. Activation and behavior. New York: Wiley; 1962.

    Google Scholar 

  100. Malmo RB. Activation: A neuropsychological dimension. Psychol Rev. 1959;66(6):367.

    CAS  PubMed  Google Scholar 

  101. Csikszentmihalyi, M., Beyond Boredom and Anxiety: Experiencing Flow in Work and Play. Behavioral science series, ed. Jossey-Bass. 1975, San Francisco: Jossey-Bass.

    Google Scholar 

  102. Von Ahn L, Dabbish L. Designing games with a purpose. Commun ACM. 2008;51(8):58–67.

    Google Scholar 

  103. Zahabi, M. and A.M. Abdul Razak, Adaptive virtual reality-based training: a systematic literature review and framework. Virtual Reality, 2020. 24(4).

    Google Scholar 

  104. Bronner S, Pinsker R, Noah JA. Physiological and psychophysiological responses in experienced players while playing different dance exer-games. Comput Human Behav. 2015;51:34–41.

    Google Scholar 

  105. Salen, K., K.S. Tekinbaş, and E. Zimmerman, Rules of play: Game design fundamentals. 2004: MIT press.

    Google Scholar 

  106. Faria AL, et al. Benefits of virtual reality based cognitive rehabilitation through simulated activities of daily living: a randomized controlled trial with stroke patients. J Neuroeng Rehabil. 2016;13(1):1–12.

    Google Scholar 

  107. Gonçalves, A., et al., A virtual reality bus ride as an ecologically valid assessment of balance: a feasibility study. Virtual Reality, 2021: p. 1–9.

    Google Scholar 

  108. Oliveira, J., et al., Virtual reality-based cognitive stimulation on people with mild to moderate dementia due to alzheimer’s disease: a randomized controlled trial. 2021.

    Google Scholar 

  109. Plotnik M, et al. Multimodal immersive trail making-virtual reality paradigm to study cognitive-motor interactions. J Neuroeng Rehabil. 2021;18(1):1–16.

    Google Scholar 

  110. Thielbar KO, et al. Home-based upper extremity stroke therapy using a multiuser virtual reality environment: a randomized trial. Arch Phys Med Rehabil. 2020;101(2):196–203.

    PubMed  Google Scholar 

  111. Pereira F, et al. The use of game modes to promote engagement and social involvement in multi-user serious games: a within-person randomized trial with stroke survivors. J Neuroeng Rehabil. 2021;18(1):1–15.

    Google Scholar 

  112. Novak D, et al. Increasing motivation in robot-aided arm rehabilitation with competitive and cooperative gameplay. J Neuroeng Rehabil. 2014;11(1):1–15.

    Google Scholar 

  113. Goršič M, Cikajlo I, Novak D. Competitive and cooperative arm rehabilitation games played by a patient and unimpaired person: effects on motivation and exercise intensity. J Neuroeng Rehabil. 2017;14(1):1–18.

    Google Scholar 

  114. Verschueren, S., C. Buffel, and G. Vander Stichele, Developing theory-driven, evidence-based serious games for health: framework based on research community insights. JMIR serious games, 2019. 7(2): p. e11565.

    Google Scholar 

  115. Max, M.N. and M.N. Sarah, A Comparative Study of Sense of Presence of Traditional Virtual Reality and Immersive Environments. Australasian Journal of Information Systems, 2016. 20(0).

    Google Scholar 

  116. Chen X, et al. ImmerTai: Immersive Motion Learning in VR Environments. J Vis Commun Image Represent. 2019;58:416–27.

    Google Scholar 

  117. Chandrasekera T, Fernando K, Puig L. Effect of Degrees of Freedom on the Sense of Presence Generated by Virtual Reality (VR) Head-Mounted Display Systems: A Case Study on the Use of VR in Early Design Studios. J Educ Technol Syst. 2019;47(4):513–22.

    Google Scholar 

  118. Shu Y, et al. Do virtual reality head-mounted displays make a difference? A comparison of presence and self-efficacy between head-mounted displays and desktop computer-facilitated virtual environments. Virtual Reality. 2019;23(4):437–46.

    Google Scholar 

  119. Ada L, et al. Task-specific training of reaching and manipulation. Adv Psychol. 1994;105:239–65.

    Google Scholar 

  120. Viau A, et al. Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis. J Neuroeng Rehabil. 2004;1(1):11.

    PubMed  PubMed Central  Google Scholar 

  121. Subramanian S, et al. Virtual reality environments for post-stroke arm rehabilitation. J Neuroeng Rehabil. 2007;4:20.

    PubMed  PubMed Central  Google Scholar 

  122. Knaut LA, et al. Kinematics of pointing movements made in a virtual versus a physical 3-dimensional environment in healthy and stroke subjects. Arch Phys Med Rehabil. 2009;90(5):793–802.

    PubMed  Google Scholar 

  123. Levin, M.F., et al., Quality of Grasping and the Role of Haptics in a 3D Immersive Virtual Reality Environment in Individuals With Stroke. IEEE Trans Neural Syst Rehabil Eng, 2015. PP(99): p. 1.

    Google Scholar 

  124. van den Hoogen W, et al. Visualizing the third dimension in virtual training environments for neurologically impaired persons: beneficial or disruptive? J Neuroeng Rehabil. 2012;9(1):73.

    PubMed  PubMed Central  Google Scholar 

  125. Lin C, Tizazu B, Woldegiorgis B. Kinematics of direct reaching in head-mounted and stereoscopic widescreen virtual environments. Virtual Reality. 2021;25:1–14.

    Google Scholar 

  126. Erhardsson, M., M. Alt Murphy, and K.S. Sunnerhagen, Commercial head-mounted display virtual reality for upper extremity rehabilitation in chronic stroke: a single-case design study. Journal of NeuroEngineering and Rehabilitation, 2020. 17(1): p. 154.

    Google Scholar 

  127. Fluet, G., et al. Sensorimotor training in virtual environments produces similar outcomes to real world training with greater efficiency. in Virtual Rehabilitation (ICVR), 2013 International Conference on. 2013. IEEE.

    Google Scholar 

  128. Laver, K.E., et al., Virtual reality for stroke rehabilitation. Cochrane database of systematic reviews, 2017(11).

    Google Scholar 

  129. Weiss PL, et al. Video capture virtual reality as a flexible and effective rehabilitation tool. J Neuroeng Rehabil. 2004;1(1):12.

    PubMed  PubMed Central  Google Scholar 

  130. Hondori HM, et al. Utility of Augmented Reality in Relation to Virtual Reality in Stroke Rehabilitation. Stroke. 2014;45:1.

    Google Scholar 

  131. Valentini, P.P., Natural interface for interactive virtual assembly in augmented reality using Leap Motion Controller. International Journal on Interactive Design and Manufacturing (IJIDeM), 2018. 12.

    Google Scholar 

  132. Bruder, G., et al. Enhancing Presence in Head-Mounted Display Environments by Visual Body Feedback Using Head-Mounted Cameras. in 2009 International Conference on CyberWorlds. 2009.

    Google Scholar 

  133. Slater, M., Implicit Learning Through Embodiment in Immersive Virtual Reality. 2017. p. 19–33.

    Google Scholar 

  134. Held JPO, et al. Augmented Reality-Based Rehabilitation of Gait Impairments: Case Report. JMIR Mhealth Uhealth. 2020;8(5): e17804.

    PubMed  PubMed Central  Google Scholar 

  135. Kim, W.S., et al., Clinical Application of Virtual Reality for Upper Limb Motor Rehabilitation in Stroke: Review of Technologies and Clinical Evidence. J Clin Med, 2020. 9(10).

    Google Scholar 

  136. Rito, D., et al. Virtual reality tools for post-stroke balance rehabilitation: a review and a solution proposal. in 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). 2021.

    Google Scholar 

  137. Thaut, M.H., G.C. McIntosh, and V. Hoemberg, Neurobiological foundations of neurologic music therapy: rhythmic entrainment and the motor system. Front Psychol, 2014. 5.

    Google Scholar 

  138. Powell, W., Virtually walking: factors influencing walking and perception of walking in treadmill-mediated virtual reality to support rehabilitation. 2011, University of Portsmouth.

    Google Scholar 

  139. Friedman, N., et al. MusicGlove: Motivating and quantifying hand movement rehabilitation by using functional grips to play music. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011. IEEE.

    Google Scholar 

  140. Brütsch K, et al. Virtual reality for enhancement of robot-assisted gait training in children with neurological gait disorders. J Rehabil Med. 2011;43(6):493–9.

    PubMed  Google Scholar 

  141. Adamovich, S., et al., Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: a proof of concept study. J Neuroeng Rehabil, 2009. 6(28).

    Google Scholar 

  142. Huegel, J.C. and M.K. O'Malley. Progressive haptic and visual guidance for training in a virtual dynamic task. in Haptics Symposium, 2010 IEEE. 2010.

    Google Scholar 

  143. 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.

    PubMed  Google Scholar 

  144. Merians AS, et al. Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis. J Neuroeng Rehabil. 2011;8:27.

    PubMed  PubMed Central  Google Scholar 

  145. Schweighofer N, et al. Task-Oriented Rehabilitation Robotics. Am J Phys Med Rehabil. 2012;91(11):S270–9.

    PubMed  Google Scholar 

  146. Khan MA, et al. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Comput Biol Med. 2020;123: 103843.

    PubMed  Google Scholar 

  147. Wolpaw JR, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–91.

    PubMed  Google Scholar 

  148. Poveda-García, A., C. Moret-Tatay, and M. Gómez-Martínez. The Association between Mental Motor Imagery and Real Movement in Stroke. in Healthcare. 2021. Multidisciplinary Digital Publishing Institute.

    Google Scholar 

  149. Stefano Filho, C.A., R. Attux, and G. Castellano, Actual, sham and no-feedback effects in motor imagery practice. Biomedical Signal Processing and Control, 2022. 71: p. 103262.

    Google Scholar 

  150. Vourvopoulos, A. and S.B. i Badia, Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. Journal of neuroengineering and rehabilitation, 2016. 13(1): p. 1–14.

    Google Scholar 

  151. Škola F, Tinková S, Liarokapis F. Progressive training for motor imagery brain-computer interfaces using gamification and virtual reality embodiment. Front Hum Neurosci. 2019;13:329.

    PubMed  PubMed Central  Google Scholar 

  152. Le Franc S, et al. Influence of the visuo-proprioceptive illusion of movement and motor imagery of the wrist on EEG cortical excitability among healthy participants. PLoS ONE. 2021;16(9): e0256723.

    PubMed  PubMed Central  Google Scholar 

  153. Chen, S., et al., The differences between motor attempt and motor imagery in BCI accuracy and event-related desynchronization of hemiplegic patients. Frontiers in Neurorobotics: p. 147.

    Google Scholar 

  154. Aggarwal S, Chugh N. Signal processing techniques for motor imagery brain computer interface: A review. Array. 2019;1: 100003.

    Google Scholar 

  155. Blanco-Mora, D.A., et al. Finding the Optimal Time Window for Increased Classification Accuracy during Motor Imagery. in BIODEVICES. 2021.

    Google Scholar 

  156. Mladenović J. Standardization of protocol design for user training in EEG-based brain–computer interface. J Neural Eng. 2021;18(1): 011003.

    Google Scholar 

  157. Vourvopoulos A, et al. Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: A clinical case report. Front Hum Neurosci. 2019;13:244.

    PubMed  PubMed Central  Google Scholar 

  158. Bütefisch CM, et al. Remote changes in cortical excitability after stroke. Brain. 2003;126(2):470–81.

    PubMed  Google Scholar 

  159. Ward NS, et al. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 2006;129(3):809–19.

    PubMed  Google Scholar 

  160. Ward N, et al. Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain. 2003;126(11):2476–96.

    CAS  PubMed  Google Scholar 

  161. Carey LM, et al. Evolution of brain activation with good and poor motor recovery after stroke. Neurorehabil Neural Repair. 2006;20(1):24–41.

    PubMed  Google Scholar 

  162. Hao, J., et al., Effects of Virtual Reality Intervention on Neural Plasticity in Stroke Rehabilitation: A Systematic Review. Arch Phys Med Rehabil, 2021.

    Google Scholar 

  163. Saleh S, et al. Mechanisms of neural reorganization in chronic stroke subjects after virtual reality training. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:8118–21.

    CAS  PubMed  Google Scholar 

  164. Saleh S, Adamovich SV, Tunik E. Resting state functional connectivity and task-related effective connectivity changes after upper extremity rehabilitation: a pilot study. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4559–62.

    PubMed  Google Scholar 

  165. Saleh S, et al. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke. Front Neurol. 2017;8:452.

    PubMed  PubMed Central  Google Scholar 

  166. Wåhlin A, et al. Rehabilitation in chronic spatial neglect strengthens resting-state connectivity. Acta Neurol Scand. 2019;139(3):254–9.

    PubMed  Google Scholar 

  167. Mekbib DB, et al. Proactive Motor Functional Recovery Following Immersive Virtual Reality-Based Limb Mirroring Therapy in Patients with Subacute Stroke. Neurotherapeutics. 2020;17(4):1919–30.

    PubMed  PubMed Central  Google Scholar 

  168. You SH, et al. Virtual reality-induced cortical reorganization and associated locomotor recovery in chronic stroke: an experimenter-blind randomized study. Stroke. 2005;36(6):1166–71.

    PubMed  Google Scholar 

  169. Jang SH, et al. Cortical reorganization and associated functional motor recovery after virtual reality in patients with chronic stroke: an experimenter-blind preliminary study. Arch Phys Med Rehabil. 2005;86(11):2218–23.

    PubMed  Google Scholar 

  170. Tunik E, Adamovich SV. Remapping in the ipsilesional motor cortex after VR-based training: a pilot fMRI study. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1139–42.

    PubMed  Google Scholar 

  171. Turolla, A., et al., Haptic-based neurorehabilitation in poststroke patients: a feasibility prospective multicentre trial for robotics hand rehabilitation. Comput Math Methods Med, 2013.

    Google Scholar 

  172. Bao X, et al. Mechanism of Kinect-based virtual reality training for motor functional recovery of upper limbs after subacute stroke. Neural Regen Res. 2013;8(31):2904–13.

    PubMed  PubMed Central  Google Scholar 

  173. Schuster-Amft C, et al. Intensive virtual reality-based training for upper limb motor function in chronic stroke: a feasibility study using a single case experimental design and fMRI. Disabil Rehabil Assist Technol. 2015;10(5):385–92.

    PubMed  Google Scholar 

  174. Xiao X, et al. Cerebral Reorganization in Subacute Stroke Survivors after Virtual Reality-Based Training: A Preliminary Study. Behav Neurol. 2017;2017:6261479.

    PubMed  PubMed Central  Google Scholar 

  175. Wang ZR, et al. Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients. Neural Regen Res. 2017;12(11):1823–31.

    PubMed  PubMed Central  Google Scholar 

  176. Fluet, G.M., Alma ; Patel, Jigna ; Van Wingerden, Anita ; Qiu, Qinyin ; Yarossi, Matthew ; Tunik, Eugene ; Adamovich, Sergei ; Massood, Supriya., Virtual reality-augmented rehabilitation for patients in sub-acute phase post-stroke: A feasibility study. Journal of Pain Management, 2016. 9: p. 227–234.

    Google Scholar 

  177. Patel J, et al. Intensive virtual reality and robotic based upper limb training compared to usual care, and associated cortical reorganization, in the acute and early sub-acute periods post-stroke: a feasibility study. J Neuroeng Rehabil. 2019;16(1):92.

    PubMed  PubMed Central  Google Scholar 

  178. Yarossi M, et al. The Association Between Reorganization of Bilateral M1 Topography and Function in Response to Early Intensive Hand Focused Upper Limb Rehabilitation Following Stroke Is Dependent on Ipsilesional Corticospinal Tract Integrity. Front Neurol. 2019;10:258.

    PubMed  PubMed Central  Google Scholar 

  179. Orihuela-Espina F, et al. Neural reorganization accompanying upper limb motor rehabilitation from stroke with virtual reality-based gesture therapy. Top Stroke Rehabil. 2013;20(3):197–209.

    PubMed  Google Scholar 

  180. Ballester BR, et al. Domiciliary VR-Based Therapy for Functional Recovery and Cortical Reorganization: Randomized Controlled Trial in Participants at the Chronic Stage Post Stroke. JMIR Serious Games. 2017;5(3): e15.

    PubMed  PubMed Central  Google Scholar 

  181. De Luca R, et al. Use of virtual reality in improving poststroke neglect: Promising neuropsychological and neurophysiological findings from a case study. Appl Neuropsychol Adult. 2019;26(1):96–100.

    PubMed  Google Scholar 

  182. Richards LG, et al. Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence. Neuropsychologia. 2008;46(1):3–11.

    PubMed  Google Scholar 

  183. Carel C, et al. Neural substrate for the effects of passive training on sensorimotor cortical representation: a study with functional magnetic resonance imaging in healthy subjects. J Cereb Blood Flow Metab. 2000;20(3):478–84.

    CAS  PubMed  Google Scholar 

  184. Jang SH, et al. Cortical reorganization induced by task-oriented training in chronic hemiplegic stroke patients. NeuroReport. 2003;14(1):137–41.

    PubMed  Google Scholar 

  185. Grefkes, C. and G.R. Fink, Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain, 2011.

    Google Scholar 

  186. Cramer SC. Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery Ann Neurol. 2008;63(3):272–87.

    PubMed  Google Scholar 

  187. Laver KE, et al. Virtual reality for stroke rehabilitation. Stroke. 2018;49(4):e160–1.

    Google Scholar 

  188. Iacoboni M, et al. Cortical mechanisms of human imitation. Science. 1999;286(5449):2526–8.

    CAS  PubMed  Google Scholar 

  189. Gallese V, et al. Action recognition in the premotor cortex. Brain. 1996;119(2):593–609.

    PubMed  Google Scholar 

  190. Grezes J, Decety J. Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta-analysis. Hum Brain Mapp. 2001;12(1):1–19.

    CAS  PubMed  Google Scholar 

  191. Rizzolatti G, Craighero L. The mirror-neuron system. Annu Rev Neurosci. 2004;27:169–92.

    CAS  PubMed  Google Scholar 

  192. Buccino G, Solodkin A, Small SL. Functions of the mirror neuron system: implications for neurorehabilitation. Cogn Behav Neurol. 2006;19(1):55–63.

    PubMed  Google Scholar 

  193. Garrison KA, Winstein CJ, Aziz-Zadeh L. The mirror neuron system: a neural substrate for methods in stroke rehabilitation. Neurorehabil Neural Repair. 2010;24(5):404–12.

    PubMed  Google Scholar 

  194. Buccino G, et al. Action observation activates premotor and parietal areas in a somatotopic manner: an fMRI study. Eur J Neurosci. 2001;13(2):400–4.

    CAS  PubMed  Google Scholar 

  195. Sale P, Franceschini M. Action observation and mirror neuron network: a tool for motor stroke rehabilitation. Eur J Phys Rehabil Med. 2012;48(2):313–8.

    CAS  PubMed  Google Scholar 

  196. Ertelt D, et al. Action observation has a positive impact on rehabilitation of motor deficits after stroke. Neuroimage. 2007;36:164–73.

    Google Scholar 

  197. Maeda F, Kleiner-Fisman G, Pascual-Leone A. Motor facilitation while observing hand actions: specificity of the effect and role of observer’s orientation. J Neurophysiol. 2002;87(3):1329–35.

    PubMed  Google Scholar 

  198. Gazzola V, et al. The anthropomorphic brain: the mirror neuron system responds to human and robotic actions. Neuroimage. 2007;35(4):1674–84.

    CAS  PubMed  Google Scholar 

  199. Ferrari PF, Rozzi S, Fogassi L. Mirror neurons responding to observation of actions made with tools in monkey ventral premotor cortex. J Cogn Neurosci. 2005;17(2):212–26.

    PubMed  Google Scholar 

  200. Modrono C, et al. Activation of the human mirror neuron system during the observation of the manipulation of virtual tools in the absence of a visible effector limb. Neurosci Lett. 2013;555:220–4.

    CAS  PubMed  Google Scholar 

  201. August, K., et al. FMRI analysis of neural mechanisms underlying rehabilitation in virtual reality: activating secondary motor areas. in Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE. 2006. IEEE.

    Google Scholar 

  202. Prochnow D, et al. A functional magnetic resonance imaging study of visuomotor processing in a virtual reality-based paradigm: Rehabilitation Gaming System. Eur J Neurosci. 2013;37(9):1441–7.

    CAS  PubMed  Google Scholar 

  203. Dobkin BH. Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol. 2007;579(3):637–42.

    CAS  PubMed  Google Scholar 

  204. Grosse-Wentrup, M., D. Mattia, and K. Oweiss, Using brain–computer interfaces to induce neural plasticity and restore function. J Neural Eng, 2011. 8(2).

    Google Scholar 

  205. Villiger M, et al. Enhanced activation of motor execution networks using action observation combined with imagination of lower limb movements. PLoS ONE. 2013;8(8): e72403.

    CAS  PubMed  PubMed Central  Google Scholar 

  206. Bermúdez i Badia, S., et al., Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training. IEEE Trans Neural Syst Rehabil Eng, 2013. 21(2): p. 174–181.

    Google Scholar 

  207. Abdollahi, F., et al., Arm control recovery enhanced by error augmentation, in IEEE Int Conf Rehabil Robot. 2011. p. 1–6.

    Google Scholar 

  208. Tunik E, Saleh S, Adamovich SV. Visuomotor discordance during visually-guided hand movement in virtual reality modulates sensorimotor cortical activity in healthy and hemiparetic subjects. IEEE Trans Neural Syst Rehabil Eng. 2013;21(2):198–207.

    PubMed  PubMed Central  Google Scholar 

  209. Lewis, C.H. and M.J. Griffin, Human factors consideration in clinical applications of virtual reality. Stud Health Technol Inform, 1997: p. 35–58.

    Google Scholar 

  210. Ballester BR, et al. The visual amplification of goal-oriented movements counteracts acquired non-use in hemiparetic stroke patients. J Neuroeng Rehabil. 2015;12:50.

    PubMed  PubMed Central  Google Scholar 

  211. Weiss PL, et al. Video capture virtual reality: A decade of rehabilitation assessment and intervention. Phys Ther Rev. 2009;14(5):307–21.

    Google Scholar 

  212. Yavuzer G, et al. “Playstation eyetoy games”improve upper extremity-related motor functioning in subacute stroke: a randomized controlled clinical trial. Eur J Phys Rehabil Med. 2008;44(3):237–44.

    CAS  PubMed  Google Scholar 

  213. da Silva Ribeiro NM, et al. Virtual rehabilitation via Nintendo Wii® and conventional physical therapy effectively treat post-stroke hemiparetic patients. Top Stroke Rehabil. 2015;22(4):299–305.

    PubMed  Google Scholar 

  214. Levac, D.E., et al. A comparison of virtual reality and active video game usage, attitudes and learning needs among therapists in Canada and the US. in 2019 International Conference on Virtual Rehabilitation (ICVR). 2019. IEEE.

    Google Scholar 

  215. Levac D, et al. Virtual reality and active videogame-based practice, learning needs, and preferences: a cross-Canada survey of physical therapists and occupational therapists. Games for health journal. 2017;6(4):217–28.

    PubMed  Google Scholar 

  216. Webster D, Celik O. Systematic review of Kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil. 2014;11(1):108.

    PubMed  PubMed Central  Google Scholar 

  217. Levac D, et al. “Kinect-ing” with clinicians: a knowledge translation resource to support decision making about video game use in rehabilitation. Phys Ther. 2015;95(3):426–40.

    PubMed  Google Scholar 

  218. Zeiler SR, Krakauer JW. The interaction between training and plasticity in the post-stroke brain. Curr Opin Neurol. 2013;26(6):609.

    PubMed  PubMed Central  Google Scholar 

  219. Gueye T, et al. Early post-stroke rehabilitation for upper limb motor function using virtual reality and exoskeleton: equally efficient in older patients. Neurol Neurochir Pol. 2021;55(1):91–6.

    PubMed  Google Scholar 

  220. Brunner I, et al. Virtual reality training for upper extremity in subacute stroke (VIRTUES): a multicenter RCT. Neurology. 2017;89(24):2413–21.

    PubMed  Google Scholar 

  221. Kiper, P., et al., Virtual reality for upper limb rehabilitation in subacute and chronic stroke: a randomized controlled trial. Archives of physical medicine and rehabilitation, 2018. 99(5): p. 834–842. e4.

    Google Scholar 

  222. Ögün MN, et al. Effect of leap motion-based 3D immersive virtual reality usage on upper extremity function in ischemic stroke patients. Arq Neuropsiquiatr. 2019;77:681–8.

    PubMed  Google Scholar 

  223. Aşkın A, et al. Effects of Kinect-based virtual reality game training on upper extremity motor recovery in chronic stroke. Somatosens Mot Res. 2018;35(1):25–32.

    PubMed  Google Scholar 

  224. Schuster-Amft C, et al. Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A multicenter parallel group randomized trial. PLoS ONE. 2018;13(10): e0204455.

    PubMed  PubMed Central  Google Scholar 

  225. Rogers JM, et al. Elements virtual rehabilitation improves motor, cognitive, and functional outcomes in adult stroke: evidence from a randomized controlled pilot study. J Neuroeng Rehabil. 2019;16(1):1–13.

    Google Scholar 

  226. Rodríguez-Hernández M, et al. Effects of Specific Virtual Reality-Based Therapy for the Rehabilitation of the Upper Limb Motor Function Post-Ictus: Randomized Controlled Trial. Brain Sci. 2021;11(5):555.

    PubMed  PubMed Central  Google Scholar 

  227. Choi, Y.-H. and N.-J. Paik, Mobile game-based virtual reality program for upper extremity stroke rehabilitation. Journal of visualized experiments: JoVE, 2018(133).

    Google Scholar 

  228. Norouzi-Gheidari N, et al. Feasibility, safety and efficacy of a virtual reality exergame system to supplement upper extremity rehabilitation post-stroke: A pilot randomized clinical trial and proof of principle. Int J Environ Res Public Health. 2020;17(1):113.

    Google Scholar 

  229. Park M, et al. Effects of virtual reality-based planar motion exercises on upper extremity function, range of motion, and health-related quality of life: a multicenter, single-blinded, randomized, controlled pilot study. J Neuroeng Rehabil. 2019;16(1):1–13.

    Google Scholar 

  230. Afsar SI, et al. Virtual reality in upper extremity rehabilitation of stroke patients: a randomized controlled trial. J Stroke Cerebrovasc Dis. 2018;27(12):3473–8.

    Google Scholar 

  231. Lee MM, Lee KJ, Song CH. Game-based virtual reality canoe paddling training to improve postural balance and upper extremity function: A preliminary randomized controlled study of 30 patients with subacute stroke. Medical science monitor: international medical journal of experimental and clinical research. 2018;24:2590.

    PubMed  Google Scholar 

  232. Fluet, G., et al., Virtual Rehabilitation of the Paretic Hand and Arm in Persons With Stroke: Translation From Laboratory to Rehabilitation Centers and the Patient's Home. Frontiers in neurology, 2021. 12.

    Google Scholar 

  233. Shiner CT, Byblow WD, McNulty PA. Bilateral Priming Before Wii-based Movement Therapy Enhances Upper Limb Rehabilitation and Its Retention After Stroke A Case-Controlled Study. Neurorehabil Neural Repair. 2014;28(9):828–38.

    PubMed  Google Scholar 

  234. Joo LY, et al. A feasibility study using interactive commercial off-the-shelf computer gaming in upper limb rehabilitation in patients after stroke. J Rehabil Med. 2010;42(5):437–41.

    Google Scholar 

  235. Mouawad MR, et al. Wii-based movement therapy to promote improved upper extremity function post-stroke: a pilot study. J Rehabil Med. 2011;43(6):527–33.

    PubMed  Google Scholar 

  236. Paquin, K., et al., Effectiveness of commercial video gaming on fine motor control in chronic stroke within community-level rehabilitation. Disabil Rehabil, 2015(0): p. 1–8.

    Google Scholar 

  237. Saposnik G, et al. Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation a pilot randomized clinical trial and proof of principle. Stroke. 2010;41(7):1477–84.

    PubMed  PubMed Central  Google Scholar 

  238. Chen, M.-H., et al., A controlled pilot trial of two commercial video games for rehabilitation of arm function after stroke. Clin Rehabili, 2014: p. 1–9.

    Google Scholar 

  239. Maier M, et al. Effect of specific over nonspecific VR-based rehabilitation on Poststroke motor recovery: a systematic meta-analysis. Neurorehabil Neural Repair. 2019;33(2):112–29.

    PubMed  PubMed Central  Google Scholar 

  240. Puh U, Hoehlein B, Deutsch JE. Validity and reliability of the Kinect for assessment of standardized transitional movements and balance: Systematic review and translation into practice. Physical Medicine and Rehabilitation Clinics. 2019;30(2):399–422.

    PubMed  Google Scholar 

  241. Pastor, I., H.A. Hayes, and S.J.M. Bamberg. A feasibility study of an upper limb rehabilitation system using kinect and computer games. in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. 2012.

    Google Scholar 

  242. Lee G. Effects of training using video games on the muscle strength, muscle tone, and activities of daily living of chronic stroke patients. J Phys Ther Sci. 2013;25(5):595.

    PubMed  PubMed Central  Google Scholar 

  243. Mohammadi R, et al. Effects of virtual reality compared to conventional therapy on balance poststroke: a systematic review and meta-analysis. J Stroke Cerebrovasc Dis. 2019;28(7):1787–98.

    PubMed  Google Scholar 

  244. Chen, L., et al., Effect of virtual reality on postural and balance control in patients with stroke: a systematic literature review. BioMed Research International, 2016. 2016.

    Google Scholar 

  245. Porras DC, et al. Advantages of virtual reality in the rehabilitation of balance and gait: systematic review. Neurology. 2018;90(22):1017–25.

    Google Scholar 

  246. Iruthayarajah J, et al. The use of virtual reality for balance among individuals with chronic stroke: a systematic review and meta-analysis. Top Stroke Rehabil. 2017;24(1):68–79.

    PubMed  Google Scholar 

  247. Garay-Sánchez A, et al. Effects of Immersive and Non-Immersive Virtual Reality on the Static and Dynamic Balance of Stroke Patients: A Systematic Review and Meta-Analysis. J Clin Med. 2021;10(19):4473.

    PubMed  PubMed Central  Google Scholar 

  248. Juras G, et al. Standards of Virtual Reality Application in Balance Training Programs in Clinical Practice: A Systematic Review. Games Health J. 2019;8(2):101–11.

    PubMed  Google Scholar 

  249. Li Z, et al. Virtual reality for improving balance in patients after stroke: a systematic review and meta-analysis. Clin Rehabil. 2016;30(5):432–40.

    PubMed  Google Scholar 

  250. Rutkowski, S., et al., Use of virtual reality-based training in different fields of rehabilitation: A systematic review and meta-analysis. Journal of Rehabilitation Medicine, 2020.

    Google Scholar 

  251. Wu J, et al. Effects of Virtual Reality Training on Upper Limb Function and Balance in Stroke Patients: Systematic Review and Meta-Meta-Analysis. J Med Internet Res. 2021;23(10): e31051.

    PubMed  PubMed Central  Google Scholar 

  252. Zhang B, et al. Virtual reality for limb motor function, balance, gait, cognition and daily function of stroke patients: A systematic review and meta-analysis. J Adv Nurs. 2021;77(8):3255–73.

    PubMed  Google Scholar 

  253. Barclay-Goddard, R., et al., Force platform feedback for standing balance training after stroke. Cochrane Database Syst Rev, 2004(4).

    Google Scholar 

  254. Sveistrup H, et al. Experimental studies of virtual reality-delivered compared to conventional exercise programs for rehabilitation. Cyberpsychol Behav. 2003;6(3):245–9.

    PubMed  Google Scholar 

  255. Bisson E, et al. Functional balance and dual-task reaction times in older adults are improved by virtual reality and biofeedback training. Cyberpsychol Behav. 2007;10(1):16–23.

    CAS  PubMed  Google Scholar 

  256. Thornton M, et al. Benefits of activity and virtual reality based balance exercise programmes for adults with traumatic brain injury: perceptions of participants and their caregivers. Brain Inj. 2005;19(12):989–1000.

    CAS  PubMed  Google Scholar 

  257. McEwen D, et al. Virtual reality exercise improves mobility after stroke: an inpatient randomized controlled trial. Stroke. 2014;45(6):1853–5.

    PubMed  Google Scholar 

  258. Gil-Gomez JA, et al. Effectiveness of a Wii balance board-based system (eBaViR) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury. J Neuroeng Rehabil. 2011;8:30.

    PubMed  PubMed Central  Google Scholar 

  259. Lloréns, R., et al., Balance rehabilitation using custom-made Wii Balance Board exercises: clinical effectiveness and maintenance of gains in an acquired brain injury population, in Int J Disabil Human Dev 2014. p. 327.

    Google Scholar 

  260. Llorens R. Effect of Virtual Reality-Based Training of the Ankle, Hip, and Stepping Strategies on Balance after Stroke. In: Virtual Reality in Health and Rehabilitation. CRC Press; 2020. p. 85–102.

    Google Scholar 

  261. Llorens R, et al. Time since injury limits but does not prevent improvement and maintenance of gains in balance in chronic stroke. Brain Inj. 2018;32(3):303–9.

    PubMed  Google Scholar 

  262. Cikajlo I, et al. Telerehabilitation using virtual reality task can improve balance in patients with stroke. Disabil Rehabil. 2012;34(1):13–8.

    PubMed  Google Scholar 

  263. Krpic A, Savanovic A, Cikajlo I. Telerehabilitation: remote multimedia-supported assistance and mobile monitoring of balance training outcomes can facilitate the clinical staff’s effort. Int J Rehabil Res. 2013;36(2):162–71.

    PubMed  Google Scholar 

  264. Sheehy L, et al. Centre of pressure displacements produced in sitting during virtual reality training in younger and older adults and patients who have had a stroke. Disabil Rehabil Assist Technol. 2020;15(8):924–32.

    PubMed  Google Scholar 

  265. Sheehy L, et al. Sitting balance exercise performed using virtual reality training on a stroke rehabilitation inpatient service: a randomized controlled study. Pm&r. 2020;12(8):754–65.

    Google Scholar 

  266. Cho KH, Lee WH. Virtual walking training program using a real-world video recording for patients with chronic stroke: a pilot study. Am J Phys Med Rehabil. 2013;92(5):371–84.

    PubMed  Google Scholar 

  267. Yang S, et al. Improving balance skills in patients who had stroke through virtual reality treadmill training. Am J Phys Med Rehabil. 2011;90(12):969–78.

    PubMed  Google Scholar 

  268. Walker ML, et al. Virtual reality-enhanced partial body weight-supported treadmill training poststroke: feasibility and effectiveness in 6 subjects. Arch Phys Med Rehabil. 2010;91(1):115–22.

    PubMed  Google Scholar 

  269. Kim N, Park Y, Lee B-H. Effects of community-based virtual reality treadmill training on balance ability in patients with chronic stroke. J Phys Ther Sci. 2015;27(3):655–8.

    PubMed  PubMed Central  Google Scholar 

  270. Kim H, et al. Virtual dual-task treadmill training using video recording for gait of chronic stroke survivors: a randomized controlled trial. J Phys Ther Sci. 2015;27(12):3693–7.

    PubMed  PubMed Central  Google Scholar 

  271. Fishbein P, et al. A preliminary study of dual-task training using virtual reality: influence on walking and balance in chronic poststroke survivors. J Stroke Cerebrovasc Dis. 2019;28(11): 104343.

    PubMed  Google Scholar 

  272. Calabrò RS, et al. The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial. J Neuroeng Rehabil. 2017;14(1):1–16.

    Google Scholar 

  273. Llorens R, et al. Improvement in balance using a virtual reality-based stepping exercise: a randomized controlled trial involving individuals with chronic stroke. Clin Rehabil. 2015;29(3):261–8.

    PubMed  Google Scholar 

  274. Henrique PP, Colussi EL, De Marchi AC. Effects of exergame on patients’ balance and upper limb motor function after stroke: a randomized controlled trial. J Stroke Cerebrovasc Dis. 2019;28(8):2351–7.

    PubMed  Google Scholar 

  275. Junata M, et al. Kinect-based rapid movement training to improve balance recovery for stroke fall prevention: a randomized controlled trial. J Neuroeng Rehabil. 2021;18(1):1–12.

    Google Scholar 

  276. Chen S-C, et al. Feasibility and effect of interactive telerehabilitation on balance in individuals with chronic stroke: a pilot study. J Neuroeng Rehabil. 2021;18(1):1–11.

    CAS  Google Scholar 

  277. Flynn S, Palma P, Bender A. Feasibility of using the Sony PlayStation 2 gaming platform for an individual poststroke: a case report. J Neurol Phys Ther. 2007;31(4):180–9.

    PubMed  Google Scholar 

  278. Deutsch, J.E., et al., Wii-Based Compared to Standard of Care Balance and Mobility Rehabilitation for Two Individuals Post-Stroke, in 2009 Virtual Rehabilitation International Conference. 2009. p. 117–120.

    Google Scholar 

  279. Fritz SL, et al. Active video-gaming effects on balance and mobility in individuals with chronic stroke: a randomized controlled trial. Top Stroke Rehabil. 2013;20(3):218–25.

    PubMed  Google Scholar 

  280. Cho KH, Lee KJ, Song CH. Virtual-reality balance training with a video-game system improves dynamic balance in chronic stroke patients. Tohoku J Exp Med. 2012;228(1):69–74.

    PubMed  Google Scholar 

  281. Barcala L, et al. Visual biofeedback balance training using wii fit after stroke: a randomized controlled trial. J Phys Ther Sci. 2013;25(8):1027–32.

    PubMed  PubMed Central  Google Scholar 

  282. Hung JW, et al. Randomized comparison trial of balance training by using exergaming and conventional weight-shift therapy in patients with chronic stroke. Arch Phys Med Rehabil. 2014;95(9):1629–37.

    PubMed  Google Scholar 

  283. Morone, G., et al., The efficacy of balance training with video game-based therapy in subacute stroke patients: a randomized controlled trial. Biomed Res Int, 2014. 2014.

    Google Scholar 

  284. Rajaratnam, B., et al., Does the Inclusion of Virtual Reality Games within Conventional Rehabilitation Enhance Balance Retraining after a Recent Episode of Stroke? Rehabil Research Pract, 2013. 2013.

    Google Scholar 

  285. Bower KJ, et al. Clinical feasibility of the Nintendo Wii for balance training post-stroke: a phase II randomized controlled trial in an inpatient setting. Clin Rehabil. 2014;28(9):912–23.

    PubMed  Google Scholar 

  286. Deutsch, J.E., Bowlby, P. G., & Kafri, M. . Effects of Optimal Standard of Care Compared with Interactive Video Gaming-Based Balance and Mobility Training for Individuals Post-stroke. in World Congress of Physical Therapy. 2011. Amsterdam, Holland.

    Google Scholar 

  287. Singh DK, et al. Effects of substituting a portion of standard physiotherapy time with virtual reality games among community-dwelling stroke survivors. BMC Neurol. 2013;13:199.

    PubMed  PubMed Central  Google Scholar 

  288. Gordon NF, et al. Physical activity and exercise recommendations for stroke survivors: an American Heart Association scientific statement from the Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention; the Council on Cardiovascular Nursing; the Council on Nutrition, Physical Activity, and Metabolism; and the Stroke Council. Circulation. 2004;109(16):2031–41.

    PubMed  Google Scholar 

  289. Rimmer JH, Wang E, Smith D. Barriers associated with exercise and community access for individuals with stroke. J Rehabil Res Dev. 2008;45(2):315–22.

    PubMed  Google Scholar 

  290. Ranky RG, et al. Modular mechatronic system for stationary bicycles interfaced with virtual environment for rehabilitation. J Neuroeng Rehabil. 2014;11:93.

    PubMed  PubMed Central  Google Scholar 

  291. Deutsch JE, et al. Feasibility of virtual reality augmented cycling for health promotion of people poststroke. J Neurol Phys Ther. 2013;37(3):118–24.

    PubMed  Google Scholar 

  292. Kafri M, et al. Energy expenditure and exercise intensity of interactive video gaming in individuals poststroke. Neurorehabil Neural Repair. 2014;28(1):56–65.

    PubMed  Google Scholar 

  293. Hurkmans HL, et al. Energy expenditure in chronic stroke patients playing Wii Sports: a pilot study. J Neuroeng Rehabil. 2011;8:38.

    PubMed  PubMed Central  Google Scholar 

  294. de Sousa JCS, et al. Aerobic stimulus induced by virtual reality games in stroke survivors. Arch Phys Med Rehabil. 2018;99(5):927–33.

    Google Scholar 

  295. Sampaio LMM, et al. Does virtual reality-based kinect dance training paradigm improve autonomic nervous system modulation in individuals with chronic stroke? J Vasc Interv Neurol. 2016;9(2):21.

    PubMed  PubMed Central  Google Scholar 

  296. Tollár J, et al. High frequency and intensity rehabilitation in 641 subacute ischemic stroke patients. Arch Phys Med Rehabil. 2021;102(1):9–18.

    PubMed  Google Scholar 

  297. Trinh T, et al. Targeted upper-limb Wii-based Movement Therapy also improves lower-limb muscle activation and functional movement in chronic stroke. Disabil Rehabil. 2017;39(19):1939–49.

    PubMed  Google Scholar 

  298. Deutsch JE, et al. Comparison of neuromuscular and cardiovascular exercise intensity and enjoyment between standard of care, off-the-shelf and custom active video games for promotion of physical activity of persons post-stroke. J Neuroeng Rehabil. 2021;18(1):1–12.

    Google Scholar 

  299. James-Palmer, A., et al. Playing self-paced video games requires the same energy expenditure but is more enjoyable and less effortful than standard of care activities. in 2019 International Conference on Virtual Rehabilitation (ICVR). 2019. IEEE.

    Google Scholar 

  300. Faria, A., et al. An integrative virtual reality cognitive-motor intervention approach in stroke rehabilitation: a pilot study. in 10th Intl Conf. Disability, Virtual Reality & Associated Technologies. 2014.

    Google Scholar 

  301. Fluet GG, et al. Does training with traditionally presented and virtually simulated tasks elicit differing changes in object interaction kinematics in persons with upper extremity hemiparesis? Top Stroke Rehabil. 2015;22(3):176–84.

    PubMed  PubMed Central  Google Scholar 

  302. Wellner M, et al. Obstacle crossing in a virtual environment with the rehabilitation gait robot LOKOMAT. Stud Health Technol Inform. 2006;125:497–9.

    Google Scholar 

  303. Cincotti F, et al. EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb. in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. 2012. IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Rehabilitation and Movement Sciences, Rutgers–The State University of New Jersey, 65 Bergen Street, Newark, NJ, 07101, USA

    Gerard G. Fluet & Devraj Roy

  2. Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain

    Roberto Llorens

  3. N-LINCS Madeira and Centro de Competências das Ciências Exatas e da Engenharia, Universidade da Madeira, Funchal, Portugal

    Sergi Bermúdez i Badia

  4. Rivers Lab: Department of Rehabilitation and Movement Sciences, Rutgers–The State University of New Jersey, 65 Bergen Street, Newark, NJ, 07101, USA

    Judith E. Deutsch

Authors
  1. Gerard G. Fluet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devraj Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberto Llorens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergi Bermúdez i Badia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Judith E. Deutsch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerard G. Fluet .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering and Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA

    David J. Reinkensmeyer

  2. Delft University of Technology, Delft, The Netherlands

    Laura Marchal-Crespo

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

    Volker Dietz

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fluet, G.G., Roy, D., Llorens, R., Bermúdez i Badia, S., Deutsch, J.E. (2022). Basis and Clinical Evidence of Virtual Reality-Based Rehabilitation of Sensorimotor Impairments After Stroke. In: Reinkensmeyer, D.J., Marchal-Crespo, L., Dietz, V. (eds) Neurorehabilitation Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-08995-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08995-4_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08994-7

  • Online ISBN: 978-3-031-08995-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation