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VR-Based Assessment and Rehabilitation of Functional Mobility

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Book cover Human Walking in Virtual Environments

Abstract

The advent of virtual reality (VR) as a tool for real-world training dates back to the mid-twentieth century and the early years of driving and flight simulators. These simulation environments, while far below the quality of today’s visual displays, proved to be advantageous to the learner due to the safe training environments the simulations provided. More recently, these training environments have proven beneficial in the transfer of user-learned skills from the simulated environment to the real world [5, 31, 48, 51, 57]. Of course the VR technology of today has come a long way. Contemporary displays boast high-resolution, wide-angle fields of view and increased portability. This has led to the evolution of new VR research and training applications in many different arenas, several of which are covered in other chapters of this book. This is true of clinical assessment and rehabilitation as well, as the field has recognized the potential advantages of incorporating VR technologies into patient training for almost 20 years [7, 10, 18, 45, 78].

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Notes

  1. 1.

    It is important to note that there are two different applications of VR in rehabilitation. When VR is used as an adjunct to rehabilitation, it is typically referred to as VR-augmented rehabilitation. Conversely, VR provided alone as a rehabilitation intervention is referred to as VR-based rehabilitation [8]. The latter is the predominant focus of this chapter.

  2. 2.

    If the visual and idiothetic information are not aligned with the user’s actions a disruption of the user’s sense of realness, or presence, in the virtual environment can result, leading to feelings of physical disorientation and even nausea [55].

  3. 3.

    Augmented reality is a tool in which the virtual world is superimposed over the real world, with the virtual information serving as a supplement to what is available in the real world alone [17].

  4. 4.

    The relation between optic flow and gait speed has been studied extensively (see text). While the findings of Prokop et al. [43] and Mohler et al. [37] parallel those of Lamontagne et al. [28], it is unclear why, exactly, the out-of-phase relation was observed. One possibility, as suggested by the authors, is that a sinusoidal change in optic flow speed may lead to a more pronounced time lag between the change in stimulus and the behavioral response. Another is that when the flow rate decreases, the participant walks faster to compensate for a perceived decrease in speed, in order to maintain a constant or preferred speed [37].

  5. 5.

    This is based on a variation of the posture-first principle [79] in which participants would prioritize locomotion on the treadmill over attending to the perceptual information on the screen in front of them.

  6. 6.

    The Multiplexing Vision Rehabilitation Device is an augmented reality device in which the user wears a see-through head-mounted display (HMD) with a \(25^\circ \) field of view to which a small monochrome video camera has been attached. When wearing the device the user not only sees the real world in full resolution, but also sees real-time edge detection from a field of view between \(75^\circ \) and \(100^\circ \), minified and displayed on the smaller field of view provided by the HMD [41].

  7. 7.

    DFA computes scaling exponents that relate a measure of variability, the detrended fluctuation function, to the time scale over which the function was computed. It is used to identify the presence or absence of persistence (i.e., a large value tends to follow a large value and a small value tends to follow a small value) in a time series. For full details, see Peng et al. [42].

  8. 8.

    A distinction must be made about the origin of the continuous information. If a computer algorithm drives the character in virtual reality, then it is presenting continuous information about walking biomechanics that is non-biological and is termed a virtual human. Alternatively, the character can be driven by the actual motion of a human in either real-time or via a recording, which is deemed biological motion and termed an avatar. Current literature has not made a distinction about which type of motion is optimal for a gait synchronization task.

  9. 9.

    RQA is a nonlinear measure that indexes repeating, or recurrent, patterns in a time series. For a review see Webber and Zbilut [71, 72].

References

  1. Apfelbaum H, Pelah A, Peli E (2007) Heading assessment by “tunnel vision” patients and control subjects standing or walking in a virtual environment. ACM Trans Appl Percept 4:1–16

    Google Scholar 

  2. Bank PJM, Roerdink M, Peper CE (2011) Comparing the efficacy of metronome beeps and stepping stones to adjust gait: steps to follow. Exp Brain Res 209:159–169

    Article  Google Scholar 

  3. Bardy BG, Warren WH, Kay BA (1996) Motion parallax is used to control postural sway during walking. Exp Brain Res 11:271–282

    Google Scholar 

  4. Blin O, Ferrandez A, Serratrice G (1990) Quantitative analysis of gait in Parkinson patients: increased variability of stride length. J Neurol Sci 98:79–91

    Article  Google Scholar 

  5. Bliss JP, Tidwell PD, Guest MA (1997) The effectiveness of virtual reality for administering spatial navigation training to firefighters. Presence: Teleoperators Virtual Environ 6:73–86

    Google Scholar 

  6. Bloem BR, Hausdorff JM, Visser JE, Giladi N (2004) Falls and freezing of gait in Parkinson’s disease: a review of two interconnected, episodic phenomena. Mov Disord 19:871–884

    Article  Google Scholar 

  7. Brooks B, McNeil JE, Rose FD (1999) Route learning in a case of amnesia: a preliminary investigation into the efficacy of training in a virtual environment. Neuropsychol Rehabil 9:63–76

    Google Scholar 

  8. Burdea GC (2003) Virtual rehabilitation—benefits and challenges. Methods Inf Med 42:519–523

    Google Scholar 

  9. Deutsch JE, Mirelman A (2007) Virtual reality-based approaches to enable walking for people poststroke. Topics Stroke Rehabil 14:45–53

    Article  Google Scholar 

  10. Emmett A (1994) Virtual reality helps steady the gait of Parkinson’s patients. Comput Graph World 17:17–18

    Google Scholar 

  11. Fung J, Richards CL, Malouin F, McFadyen BJ, Lamontagne A (2006) A treadmill and motion coupled virtual reality system for gait training post-stroke. CyberPsychol Behav 9:157–162

    Article  Google Scholar 

  12. Gerin-Lajoie M, Ciombor DM, Warren WH, Aaron RK (2010) Using ambulatory virtual environments for the assessment of functional gait impairment: a proof-of-concept study. Gait Posture 31:533–536

    Article  Google Scholar 

  13. Geruschat DR, Turano KA, Stahl JW (1998) Traditional measures of mobility performance and retinitis pigmentosa. Optom Vis Sci 75:525–537

    Article  Google Scholar 

  14. Gibson JJ (1950) The perception of the visual world. Houghton Mifflin, Boston

    Google Scholar 

  15. Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, Boston

    Google Scholar 

  16. Glass L, Mackey MC (1979) Pathological conditions resulting from instabilities in physiological control systems. Ann N Y Acad Sci 316:214–235

    Article  Google Scholar 

  17. Gobbetti E, Scatenis R (1998) Virtual reality: past, present and future. In: Riva G, Wiederhold BK, Molinari E (eds) Virtual environments in clinical psychology and neuroscience. Ios Press, Amsterdam, pp 1–18

    Google Scholar 

  18. Goldberg S (1994) Training dismounted soldiers in a distributed interactive virtual environment. U.S. Army Res Inst Newslett 14:9–12

    Google Scholar 

  19. Grabiner PC, Biswas ST, Grabiner MD (2001) Age-related changes in spatial and temporal gait variables. Arch Phys Med Rehabil 82:31–35

    Article  Google Scholar 

  20. Hausdorff JM (2007) Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking. Hum Mov Sci 26:555–589

    Article  Google Scholar 

  21. Hausdorff JM, Peng C-K, Ladin Z, Wei JY, Goldberger AL (1995) Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. Model Physiol 78:349–358

    Google Scholar 

  22. Haymes S, Guest DM, Heyes A, Johnston A (1996) Mobility of people with retinitis pigmentosa as a function of vision and psychological variables. Optom Vis Sci 73:621–637

    Article  Google Scholar 

  23. Hove MJ, Spivey MJ, Krumhansl CL (2010) Compatibility of motion facilitates visuomotor synchronization. J Exp Psychol: Hum Percept Perform 36:1525–1534

    Article  Google Scholar 

  24. Jaffe DL, Brown DA, Pierson-Carey CD, Buckley EL, Lew HL (2004) Stepping over obstacles to improve walking in individuals with poststroke hemiplegia. J Rehabil Res Dev 41:283–292

    Article  Google Scholar 

  25. Kiefer AW, Bruggeman H, Woods R, Warren W (2012) Obstacle detection during walking by patients with tunnel vision. J Vis 12:183

    Article  Google Scholar 

  26. Kizony R, Katz N, Weiss PL (2004) Virtual reality based intervention in rehabilitation: relationship between motor and cognitive abilities and performance within virtual environments for patients with stroke. In: Proceedings of the 5th international conference on disability, virtual reality and associated technology. Oxford, UK

    Google Scholar 

  27. Kuyk T, Elliott JL, Biehl J, Fuhr PS (1996) Environmental variables and mobility performance in adults with low vision. J Am Optom Assoc 67:403–409

    Google Scholar 

  28. Lamontagne A, Fung J, McFadyen BJ, Faubert J (2007) Modulation of walking speed by changing optic flow in persons with stroke. J NeuroEng Rehabil 4:22–30

    Article  Google Scholar 

  29. Li L, Peli E, Warren WH (2002) Heading perception in patients with advanced retinitis pigmentosa. Optom Vis Sci 79:581–589

    Article  Google Scholar 

  30. Lim I, Wegen EV, Goede CD, Deutekom M, Nieuwboer A, Willems A, Kwakkel G (2005) Effects of external rhythmical cueing on gait in patients with Parkinson’s disease: a systematic review. Clin Rehabil 19:695–713

    Article  Google Scholar 

  31. Lintern G, Roscoe JM, Koonce JM, Jefferson M, Segal LD (1990) Transfer of landing skills in beginning flight simulation. Human Factors 32:319–327

    Google Scholar 

  32. Lovie-Kitchin JE, Mainstone JC, Robinson J, Brown B (1990) What areas of the visual field are important for mobility in low vision patients? Clin Vis Sci 5:249–263

    Google Scholar 

  33. Luo G, Woods RL, Peli E (2009) Collision judgment when using an augmented-vision head-mounted display device. Invest Ophthalmol Vis Sci 50:4509–4515

    Article  Google Scholar 

  34. Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197:287–289

    Article  Google Scholar 

  35. McIntosh GC, Brown SH, Rice RR, Thaut MH (1997) Rhythmic auditory-motor facilitation of gait patterns in patients with Parkinson’s disease. J Neurol Neurosurg Psychiatry 62:22–26

    Article  Google Scholar 

  36. Mirelman A, Maidan I, Herman T, Deutsch JE, Giladi N, Hausdorff JM (2011) Virtual reality for gait training: can it induce motor learning to enhance complex walking and reduce fall risk in patients with Parkinson’s disease? J Gerontol A: Biol Sci Med Sci 66A:234–240

    Article  Google Scholar 

  37. Mohler BJ, Thompson WB, Creem-Regehr SH, Willemsen P, Pick HL et al (2007) Calibration of locomotion resulting from visual motion in a treadmill=based virtual environment. ACM Trans Appl Percept 4:1–17

    Article  Google Scholar 

  38. Olney SJ, Richards C (1996) Hemiparetic gait following stroke. Part I: characteristics. Gait Posture 4:136–148

    Article  Google Scholar 

  39. Owings T, Grabiner M (2004) Step width variability, but not step length variability or step time variability, discriminates gait of healthy young and older adults during treadmill locomotion. J Biomech 37:935–938

    Article  Google Scholar 

  40. Pailhous J, Ferrandez A, Fluckiger M, Baumberger B (1990) Unintentional modulations of human gait by optical flow. Behav Brain Res 38:275–281

    Article  Google Scholar 

  41. Peli E (2001) Vision multiplexing: an engineering approach to vision rehabilitation device development. Optom Vis Sci 78:304–315

    Article  Google Scholar 

  42. Peng CK, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82–87

    Article  Google Scholar 

  43. Prokop T, Schubert M, Berger W (1997) Visual influence on human locomotion: modulation to changes in optic flow. Exp Brain Res 114:63–70

    Article  Google Scholar 

  44. Riess TJ (1998) Gait and Parkinson’s disease: a conceptual model for an augmented-reality based therapeutic device. In: Riva G, Wiederhold BK, Molinari E (eds) Virtual environments in clinical psychology and neuroscience. Ios Press, Amsterdam, pp 200–208

    Google Scholar 

  45. Riess TWS et al (1995) Augmented reality in the treatment of Parkinson’s disease. In: Morgan K, Satava M, Sieburg HB (eds) Interactive technology and the new paradigm for healthcare. IOS Press, Amsterdam, pp 298–302

    Google Scholar 

  46. Rhea CK, Gérin-Lajoie M, Ciombor DMcK, Warren WH, Aaron RK, Recurrence quantification analysis of walking path trajectories in a functional mobility task with imposed constraints. In: Paper presented at the North American Society for Psychology in Sport and Physical Activity, Tucson, AZ

    Google Scholar 

  47. Rhea C, Kiefer A, Warren W, D’Andrea S, Aaron R (2011) Synchronizing to a “noisy” metronome induces corresponding shifts in fractal gait dynamics. In: Paper presented at the North American Society for Psychology in Sport and Physical Activity, Burlington, VT

    Google Scholar 

  48. Rose FD, Attree EA, Brooks BM (2000) Training in virtual environments: transfer to real world tasks and equivalence to real task training. Ergonomics 43:494–511

    Article  Google Scholar 

  49. Schubert M, Prokop T, Brocke F, Berger W (2005) Visual kinesthesia and locomotion in Parkinson’s disease. Mov Disord 2:141–150

    Article  Google Scholar 

  50. Shalvoy RM, Bruggeman H, D’Andrea S, Warren W, Aaron RK (2013) Virtual environmental navigation to quantify functional disability in ACL-deficient knees. In: Paper presented at the Orthopedic Research Society, San Antonio, TX

    Google Scholar 

  51. Seymour NE, Gallagher AG, Roman SA, O’Brien MK, Bansal VK, Andersen DK, Satava RM (2002) Virtual reality training improves operations room performance. Ann Surg 236:458–464

    Article  Google Scholar 

  52. Spaulding SJ, Livingston LA, Hartsell HD (2003) The influence of external orthotic support on the adaptive gait characteristics of individuals with chronically unstable ankles. Gait Posture 17:152–153

    Article  Google Scholar 

  53. Stephen DG, Stepp N, Dixon JA, Turvey MT (2008) Strong anticipation: sensitivity to long-range correlations in synchronization behavior. Phys A 387:5271–5278

    Article  Google Scholar 

  54. Stergiou N, Decker LM (2011) Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 30:869–888

    Google Scholar 

  55. Tarr MJ, Warren WH (2002) Virtual reality in behavioral neuroscience and beyond. Nat Neurosci 5:1089–1092

    Article  Google Scholar 

  56. Thaut MH, McIntosh C, Rice R, Miller RA, Rathbun J, Brault JM (1996) Rhythmic auditory stimulation in gait training for Parkinson’s disease patients. Mov Disord 11:193–200

    Article  Google Scholar 

  57. Torkington J, Smith SGT, Rees BI, Darzi A (2001) Skill transfer from virtual reality to a real laparoscopic task. Surg Endosc 15:1076–1079

    Article  Google Scholar 

  58. Turano KA, Geruschat DR, Baker FH, Stahl JW, Shapiro MD (2001) Direction of gaze while walking a simple route: persons with normal vision and persons with retinitis pigmentosa. Optom Vis Sci 78:667–675

    Article  Google Scholar 

  59. Turano KA, Yu D, Hao L, Hicks JC (2005) Optic-flow and egocentric-direction strategies in walking: central vs. peripheral visual field. Vis Res 45:3117–3132

    Article  Google Scholar 

  60. van Wegen E, de Goede C, Lim I, Rietberg M, Nieuwboer A, Willems A, Jones D (2006) The effect of rhythmic somatosensory cueing on gait in patients with Parkinson’s disease. J Neurol Sci 248:210–214

    Article  Google Scholar 

  61. Van Orden GC (2007) The fractal picture of health and wellbeing. Psychol Sci Agenda 21. http://www.apa.org/science/psa/vanorden.html. Accessed 21 Sept 2008

  62. Varraine E, Bonnard M, Pailhous J (2002) Interaction between different sensory cues in the control of human gait. Exp Brain Res 142:374–384

    Article  Google Scholar 

  63. von Schroeder HP, Coutts RD, Lyden PD, Billings E, Nickel VL (1995) Gait parameters following stroke: a practical assessment. J Rehabil Res Dev 32:25–31

    Google Scholar 

  64. von Porat A, Henriksson M, Holmstrom E, Thorstensson CA, Mattsson L, Roos EM (2006) Knee kinematics and kinetics during gait, step and hop in males with a 16 years old ACL injury compared with matched controls. Knee Surg Sports Traumatol Arthrosc 14:546–554

    Article  Google Scholar 

  65. Warren WH (1995) Constructing an econiche. In: Flach J, Hancock P, Caird J, Vicente K (eds) The ecology of human-machine systems. Erlbaum, Hilldsale, pp 210–237

    Google Scholar 

  66. Warren WH (2006) The dynamics of perception and action. Psychol Rev 113:358–389

    Article  Google Scholar 

  67. Warren WH (2008) Optic flow. In: Basbaum AI, Kaneko A, Shepherd GM, Westheimer G (eds) The senses—a comprehensive reference: vision II (vol 2, Albright TD, Masland R, eds). Academic Press, Oxford, pp 219–230

    Google Scholar 

  68. Warren WH, Kay BA, Duchon AP, Zosh W, Sahuc S (2001) Optic flow is used to control human walking. Nat Neurosci 4:213–216

    Article  Google Scholar 

  69. Warren WH, Kay BA, Yilmaz E (1996) Visual control of posture during walking: functional specificity. J Exp Psychol: Hum Percept Perform 22:818–838

    Article  Google Scholar 

  70. Warren WH, Morris M, Kalish M (1988) Perception of translational heading from optical flow. J Exp Psychol: Hum Percept Perform 14:646–660

    Article  Google Scholar 

  71. Webber CL, Zbilut JP (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76:965–973

    Google Scholar 

  72. Webber CL, Zbilut JP (2005) Recurrence quantification analysis of nonlinear dynamical systems. In: Riley MA, Van Orden GC (eds). Tutorials in contemporary nonlinear methods for the behavioral sciences. National Science Foundation, Arlington, pp 353–400. http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp. Accessed 28 Sept 2006

  73. Webster KE, Merory JR, Wittwer JE (2006) Gait variability in community dwelling adults with Alzheimer disease. Alzheimer Dis Assoc Disord 20:37–40

    Article  Google Scholar 

  74. Weiss PL, Kizony R, Feintuch U, Katz N (2006) Virtual reality in neurorehabilitation. In: Selzer ME, Cohen L, Gage FH, Clarke S, Duncan PW (eds) Textbook of neural repair and neurorehabilitation, vol 2. University Press, Cambridge

    Google Scholar 

  75. Weiss PL, Rand D, Katz N, Kizony R (2004) Video capture virtual reality as a flexible and effective rehabilitation tool. J Rehabil Res Dev 1:12

    Google Scholar 

  76. West BJ (2006) Where medicine went wrong: rediscovering the path to complexity. World Scientific Publishing Co. Pte. Ltd., Hackensack

    Google Scholar 

  77. Willems AM, Nieuwboer A, Chavret F, Desloovere K, Dom R, Rochester L, Wegen EV (2006) The use of rhythmic auditory cues to influence gait in patients with Parkinson’s disease, the differential effect for freezers and non-freezers, an explorative study. Disabil Rehabil 28:721–728

    Google Scholar 

  78. Wilson PN, Foreman N, Tlauka M (1996) Transfer of spatial information from a virtual to a real environment in physically disabled children. Disabil Rehabil 18:633–637

    Article  Google Scholar 

  79. Woollacott M, Shumway-Cook A (2002) Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture 16:1–14

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Box 1821, Providence, RI, 02912-1821, USA

    Adam W. Kiefer & William H. Warren

  2. Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC, USA

    Christopher K. Rhea

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  1. Adam W. Kiefer
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Correspondence to Adam W. Kiefer .

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Editors and Affiliations

  1. University of Würzburg, Oswald-Külpe-Weg 82, Würzburg, 97074, Germany

    Frank Steinicke

  2. Drexel University, Department of Electrical Engineering and Computer Engineerin, Philadelphia, 19104, Pennsylvania, USA

    Yon Visell

  3. University of Toronto,  Toronto Rehabilitation Institute, Univ, University Ave 550, Toronto, M5G 2A2, Ontario, Canada

    Jennifer Campos

  4. Computer Science and Control (INRIA), National Institute for Research in, Campus de Beaulie, Rennes Cedex, 35042, France

    Anatole Lécuyer

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Kiefer, A.W., Rhea, C.K., Warren, W.H. (2013). VR-Based Assessment and Rehabilitation of Functional Mobility. In: Steinicke, F., Visell, Y., Campos, J., Lécuyer, A. (eds) Human Walking in Virtual Environments. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8432-6_15

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