Usability and performance of leap motion and oculus rift for upper arm virtual reality stroke rehabilitation

Research output: Contribution to journalArticle

Abstract

Intensive rehabilitation is important for stroke survivors but difficult to achieve due to limited access to rehabilitation therapy. We present a virtual reality rehabilitation system, Target Acquiring Exercise (TAGER), designed to supplement center-based rehabilitation therapy by providing engaging and personalized exercises. TAGER uses natural user interface devices, namely the Microsoft Kinect, Leap Motion and Myo armband, to track upper arm and body motion. Linear regression was applied to 3D user motion data using four popular forms of Fitts’s law and each approach evaluated. While all four forms of Fitt’s Law produced similar results and could model users adequately, it may be argued that a 3D tailored form provided the best fit. However, we propose that Fitts’s Law may be more suitable as the basis of a more complex model to profile user performance. Evaluated by healthy users TAGER proved robust, with valuable lessons learned to inform future design. The majority of users enjoyed using the Leap Motion controller and VR Headset, the inclusion of visual cues shows a general improvement in target acquiring performance and that Fitts’s Law can be used to linearly model user reaching and pointing movements in 3D environments. However, in some situations this isn’t the case, emphasizing the importance of user profiling to examine the user's kinematic behavior more intricately.
LanguageEnglish
Pages1-10
JournalJournal of Alternative Medicine Research
Volume9
Issue number4
Publication statusAccepted/In press - 15 Feb 2017

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Patient rehabilitation
Virtual reality
Linear regression
User interfaces
Kinematics
Controllers

Keywords

  • Virtual reality
  • leap motion
  • oculus
  • stroke
  • rehabilitation
  • usability

Cite this

@article{86accfccaa2a4b2e9abbfac14afb01e9,
title = "Usability and performance of leap motion and oculus rift for upper arm virtual reality stroke rehabilitation",
abstract = "Intensive rehabilitation is important for stroke survivors but difficult to achieve due to limited access to rehabilitation therapy. We present a virtual reality rehabilitation system, Target Acquiring Exercise (TAGER), designed to supplement center-based rehabilitation therapy by providing engaging and personalized exercises. TAGER uses natural user interface devices, namely the Microsoft Kinect, Leap Motion and Myo armband, to track upper arm and body motion. Linear regression was applied to 3D user motion data using four popular forms of Fitts’s law and each approach evaluated. While all four forms of Fitt’s Law produced similar results and could model users adequately, it may be argued that a 3D tailored form provided the best fit. However, we propose that Fitts’s Law may be more suitable as the basis of a more complex model to profile user performance. Evaluated by healthy users TAGER proved robust, with valuable lessons learned to inform future design. The majority of users enjoyed using the Leap Motion controller and VR Headset, the inclusion of visual cues shows a general improvement in target acquiring performance and that Fitts’s Law can be used to linearly model user reaching and pointing movements in 3D environments. However, in some situations this isn’t the case, emphasizing the importance of user profiling to examine the user's kinematic behavior more intricately.",
keywords = "Virtual reality, leap motion, oculus, stroke, rehabilitation, usability",
author = "Dominic Holmes and D.K. Charles and PJ Morrow and S McClean and McDonough, {S M}",
note = "Reference text: 1. Kwakkel G, Wagenaar R, Twisk J, Lankhorst G, Koetsier J. Intensity of leg and arm training after primary middle-cerebral-artery stroke: a randomised trial. Lancet 1999;354(9174):191–6. 2. B{\"u}tefisch C, Hummelsheim H, Denzler P. Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J Neurol Sci 1995;130:59–68. 3. Crosbie J, Lennon S, Basford J, McDonough S. Virtual reality in stroke rehabilitation: Still more virtual than real. Disabil Rehabil 2007;29(14):1139–46. 4. Laver K, S G, S T, Je D, M C. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2015;2:8,11,12,13. 5. Holmes D, Charles D, Morrow P, McClean S, McDonough S. Usability and Performance of Leap Motion and Oculus Rift for Upper Arm Virtual Reality Stroke Rehabilitation. 11th Intl Conf. Disability, Virtual Reality and Associated Technologies, 2016:217–26. 6. Burke J, McNeill M, Charles D, Morrow P, Crosbie J, McDonough S. Serious Games for Upper Limb Rehabilitation Following Stroke. 2009 Conference in Games and Virtual Worlds for Serious Applications. Ieee, 2009:103–10. 7. Charles D, Pedlow K, McDonough S, Shek K, Charles T. Close range depth sensing cameras for virtual reality based hand rehabilitation. J Assist Technol 2014;8(3):138–49. 8. Levin MF, Weiss PL, Keshner E a. Emergence of virtual reality as a tool for upper limb rehabilitation. Phys Ther 2015;95(3):415–25. 9. Cameirao M, Bermudez i Badia S, Verschure P. Virtual Reality Based Upper Extremity Rehabilitation Following Stroke: A Review. J CyberTherapy Rehabil 2008;1(1):63–74. 10. Avanzini F, De Gotzen A, Spagnol S, Roda A. Integrating auditory feedback in motor rehabilitation systems. Proc Int Conf Multimodal Interfaces Ski Transf 2009;232:53–8. 11. Karime A, Eid M, Alja’am JM, Saddik A El, Gueaieb W. A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training. IEEE Trans Instrum Meas 2014;63(1):135–44. 12. Zimmerli L, Krewer C, Gassert R, M{\"u}ller F, Riener R, L{\"u}nenburger L. Validation of a mechanism to balance exercise difficulty in robot-assisted upper-extremity rehabilitation after stroke. J Neuroeng Rehabil 2012;9(1):6. 13. Murata A, Iwase H. Extending Fitts’ law to a three-dimensional pointing task. Hum Mov Sci 2001;20(6):791–805. 14. Hochstenbach-Waelen A, Seelen H a M. Embracing change: practical and theoretical considerations for successful implementation of technology assisting upper limb training in stroke. J Neuroeng Rehabil 2012;9(1):52. 15. Powell V, Powell WA. Locating objects in virtual reality – the effect of visual properties on target acquisition in unrestrained reaching. Intl Conf Disabil Virtual Real Assoc Technol 2014;2–4. 16. Cha Y, Rohae M. Extended Fitts’ law for 3D pointing tasks using 3D target arrangements. Int J Ind Ergon 2013;43(4):350–5. 17. Heiko D. A Lecture on Fitts ’ Law [Internet]. 2013:19–25. URL: http://www.cip.ifi.lmu.de/~drewes/science/fitts/A Lecture on Fitts Law.pdf This paper has also been included in the publisher's yearbook: Alternative Medicine Research Yearbook 2017 Editor: Joan Merrick ISBN: 979-1-53613-726-2 Copyright 2018 Nova Science Publishers, Inc.",
year = "2017",
month = "2",
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TY - JOUR

T1 - Usability and performance of leap motion and oculus rift for upper arm virtual reality stroke rehabilitation

AU - Holmes, Dominic

AU - Charles, D.K.

AU - Morrow, PJ

AU - McClean, S

AU - McDonough, S M

N1 - Reference text: 1. Kwakkel G, Wagenaar R, Twisk J, Lankhorst G, Koetsier J. Intensity of leg and arm training after primary middle-cerebral-artery stroke: a randomised trial. Lancet 1999;354(9174):191–6. 2. Bütefisch C, Hummelsheim H, Denzler P. Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J Neurol Sci 1995;130:59–68. 3. Crosbie J, Lennon S, Basford J, McDonough S. Virtual reality in stroke rehabilitation: Still more virtual than real. Disabil Rehabil 2007;29(14):1139–46. 4. Laver K, S G, S T, Je D, M C. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2015;2:8,11,12,13. 5. Holmes D, Charles D, Morrow P, McClean S, McDonough S. Usability and Performance of Leap Motion and Oculus Rift for Upper Arm Virtual Reality Stroke Rehabilitation. 11th Intl Conf. Disability, Virtual Reality and Associated Technologies, 2016:217–26. 6. Burke J, McNeill M, Charles D, Morrow P, Crosbie J, McDonough S. Serious Games for Upper Limb Rehabilitation Following Stroke. 2009 Conference in Games and Virtual Worlds for Serious Applications. Ieee, 2009:103–10. 7. Charles D, Pedlow K, McDonough S, Shek K, Charles T. Close range depth sensing cameras for virtual reality based hand rehabilitation. J Assist Technol 2014;8(3):138–49. 8. Levin MF, Weiss PL, Keshner E a. Emergence of virtual reality as a tool for upper limb rehabilitation. Phys Ther 2015;95(3):415–25. 9. Cameirao M, Bermudez i Badia S, Verschure P. Virtual Reality Based Upper Extremity Rehabilitation Following Stroke: A Review. J CyberTherapy Rehabil 2008;1(1):63–74. 10. Avanzini F, De Gotzen A, Spagnol S, Roda A. Integrating auditory feedback in motor rehabilitation systems. Proc Int Conf Multimodal Interfaces Ski Transf 2009;232:53–8. 11. Karime A, Eid M, Alja’am JM, Saddik A El, Gueaieb W. A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training. IEEE Trans Instrum Meas 2014;63(1):135–44. 12. Zimmerli L, Krewer C, Gassert R, Müller F, Riener R, Lünenburger L. Validation of a mechanism to balance exercise difficulty in robot-assisted upper-extremity rehabilitation after stroke. J Neuroeng Rehabil 2012;9(1):6. 13. Murata A, Iwase H. Extending Fitts’ law to a three-dimensional pointing task. Hum Mov Sci 2001;20(6):791–805. 14. Hochstenbach-Waelen A, Seelen H a M. Embracing change: practical and theoretical considerations for successful implementation of technology assisting upper limb training in stroke. J Neuroeng Rehabil 2012;9(1):52. 15. Powell V, Powell WA. Locating objects in virtual reality – the effect of visual properties on target acquisition in unrestrained reaching. Intl Conf Disabil Virtual Real Assoc Technol 2014;2–4. 16. Cha Y, Rohae M. Extended Fitts’ law for 3D pointing tasks using 3D target arrangements. Int J Ind Ergon 2013;43(4):350–5. 17. Heiko D. A Lecture on Fitts ’ Law [Internet]. 2013:19–25. URL: http://www.cip.ifi.lmu.de/~drewes/science/fitts/A Lecture on Fitts Law.pdf This paper has also been included in the publisher's yearbook: Alternative Medicine Research Yearbook 2017 Editor: Joan Merrick ISBN: 979-1-53613-726-2 Copyright 2018 Nova Science Publishers, Inc.

PY - 2017/2/15

Y1 - 2017/2/15

N2 - Intensive rehabilitation is important for stroke survivors but difficult to achieve due to limited access to rehabilitation therapy. We present a virtual reality rehabilitation system, Target Acquiring Exercise (TAGER), designed to supplement center-based rehabilitation therapy by providing engaging and personalized exercises. TAGER uses natural user interface devices, namely the Microsoft Kinect, Leap Motion and Myo armband, to track upper arm and body motion. Linear regression was applied to 3D user motion data using four popular forms of Fitts’s law and each approach evaluated. While all four forms of Fitt’s Law produced similar results and could model users adequately, it may be argued that a 3D tailored form provided the best fit. However, we propose that Fitts’s Law may be more suitable as the basis of a more complex model to profile user performance. Evaluated by healthy users TAGER proved robust, with valuable lessons learned to inform future design. The majority of users enjoyed using the Leap Motion controller and VR Headset, the inclusion of visual cues shows a general improvement in target acquiring performance and that Fitts’s Law can be used to linearly model user reaching and pointing movements in 3D environments. However, in some situations this isn’t the case, emphasizing the importance of user profiling to examine the user's kinematic behavior more intricately.

AB - Intensive rehabilitation is important for stroke survivors but difficult to achieve due to limited access to rehabilitation therapy. We present a virtual reality rehabilitation system, Target Acquiring Exercise (TAGER), designed to supplement center-based rehabilitation therapy by providing engaging and personalized exercises. TAGER uses natural user interface devices, namely the Microsoft Kinect, Leap Motion and Myo armband, to track upper arm and body motion. Linear regression was applied to 3D user motion data using four popular forms of Fitts’s law and each approach evaluated. While all four forms of Fitt’s Law produced similar results and could model users adequately, it may be argued that a 3D tailored form provided the best fit. However, we propose that Fitts’s Law may be more suitable as the basis of a more complex model to profile user performance. Evaluated by healthy users TAGER proved robust, with valuable lessons learned to inform future design. The majority of users enjoyed using the Leap Motion controller and VR Headset, the inclusion of visual cues shows a general improvement in target acquiring performance and that Fitts’s Law can be used to linearly model user reaching and pointing movements in 3D environments. However, in some situations this isn’t the case, emphasizing the importance of user profiling to examine the user's kinematic behavior more intricately.

KW - Virtual reality

KW - leap motion

KW - oculus

KW - stroke

KW - rehabilitation

KW - usability

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VL - 9

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ER -