Augmented Reality-Assisted Healthcare Exercising Systems

  • Chapter
  • First Online:
Springer Handbook of Augmented Reality

Abstract

The number of people with low upper-extremity functions is increasing due to the sedentary lifestyle, muscular disuse, and aging of population. Therefore, healthcare exercising systems that aim to enhance upper-extremity skills are desirable. The improvement of motor functions is an ordered process, and hence, the development of an upper-extremity training plan with stages with respect to the capability of the users is an important issue. Augmented Reality (AR) -assisted motor-skills training applications have been proven to be effective. This chapter discusses the importance of providing AR-assisted healthcare exercises in stages. The chapter reviews the current AR-assisted healthcare exercising systems and makes a comparison with virtual reality-based systems as well as conventional systems. A novel AR-assisted Three-stage Healthcare Exercising system (ARTHE) is presented to demonstrate stage-based AR-assisted systems for training activities of daily living.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 298.53
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 385.19
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cheng, Y.Y., Hsieh, W.L., Kao, C.L., Chan, R.C.: Principles of rehabilitation for common chronic neurologic diseases in the elderly. J. Clin. Gerontol. Geriatr. 3(1), 5–13 (2012)

    Google Scholar 

  2. Siparsky, P.N., Kirkendall, D.T., Garrett, W.E.: Muscle changes in aging: understanding sarcopenia. Sports Health. 6(1), 36–40 (2014)

    Google Scholar 

  3. Steffen, D., Bleser, G., Weber, M., Stricker, D., Fradet, L., Marin, F.: A personalized exercise trainer for elderly. In: Proceedings of the 5th International Conference on Pervasive Computing Technologies for Healthcare, Dublin, pp. 24–31 (2011)

    Google Scholar 

  4. Crow, J.L., Harmeling-van der Wel, B.C.: Hierarchical properties of the motor function sections of the Fugl-Meyer assessment scale for people after stroke: a retrospective study. Phys. Ther. 88, 1554–1567 (2008)

    Google Scholar 

  5. Naghdi, S., Ansari, N.N., Mansouri, K., Hasson, S.: A neurophysiological and clinical study of Brunnstrom recovery stages in the upper limb following stroke. Brain Inj. 24, 1372–1378 (2010)

    Google Scholar 

  6. Twitchell, T.E.: The restoration of motor function following hemiplegia in man. Brain. 74(4), 443–480 (1951)

    Google Scholar 

  7. Morone, G., Spitoni, G.F., De Bartolo, D., Ghooshchy, S.G., Di Iulio, F., Paolucci, S., Zoccolotti, P., Iosa, M.: Rehabilitative devices for a top-down approach. Expert Rev. Med. Devices. 16(3), 187–195 (2019)

    Google Scholar 

  8. Foulds, R.A., Saxe, D.M., Joyce, A.W., Adamovich, S.: Sensory-motor enhancement in a virtual therapeutic environment. Virtual Real. 12(2), 87–97 (2008)

    Google Scholar 

  9. Zhao, M.Y., Ong, S.K., Nee, A.Y.C.: An augmented reality-assisted therapeutic healthcare exercise system based on bare-hand interaction. Int. J. Human Comput. Interact. 32(9), 708–721 (2016)

    Google Scholar 

  10. Crocetta, T.B., de Araújo, L.V., Guarnieri, R., Massetti, T., Ferreira, F.H.I.B., de Abreu, L.C., de Mello Monteiro, C.B.: Virtual reality software package for implementing motor learning and rehabilitation experiments. Virtual Real. 22(3), 199–209 (2018)

    Google Scholar 

  11. Ma, S., Varley, M., Shark, L.K., Richards, J.: Overcoming the information overload problem in a multiform feedback-based virtual reality system for hand motion rehabilitation: healthy subject case study. Virtual Real. 16(4), 325–334 (2012)

    Google Scholar 

  12. Shen, Y., Gu, P., Ong, S.K., Nee, A.Y.C.: A novel approach in rehabilitation of hand-eye coordination and finger dexterity. Virtual Real. 16, 161–171 (2012)

    Google Scholar 

  13. Aung, Y.M., Al-Jumaily, A.: AR based upper limb rehabilitation system in biomedical robotics and biomechatronics (BioRob). In: Proceedings of the 4th IEEE RAS & EMBS International Conference, Rome, pp. 213–218 (2012)

    Google Scholar 

  14. ALS worldwide. Range of Motion Exercises. https://alsworldwide.org/assets/misc/RANGE_OF_MOTION_EXERCISES_WITH_PHOTOS_copy.pdf. Accessed 6 Feb 2020

  15. French, B., Thomas, L., Leathley, M., Sutton, C., McAdam, J., Forster, A., Watkins, C.: Does repetitive task training improve functional activity after stroke? A Cochrane systematic review and meta-analysis. J. Rehabil. Med. 42(1), 9–15 (2010)

    Google Scholar 

  16. Woldag, H., Stupka, K., Hummelsheim, H.: Repetitive training of complex hand and arm movements with sha** is beneficial for motor improvement in patients after stroke. J. Rehabil. Med. 42(6), 582–587 (2010)

    Google Scholar 

  17. Muellbacher, W., Ziemann, U., Boroojerdi, B., Cohen, L., Hallett, M.: Role of the human motor cortex in rapid motor learning. Exp. Brain Res. 136(4), 431–438 (2001)

    Google Scholar 

  18. Carr, J.H., Shepherd, R.B.: Neurological Rehabilitation, 2nd edn. Elsevier/Churchill Livingstone, London (2010)

    Google Scholar 

  19. Chan, D.Y., Chan, C.C., Au, D.K.: Motor relearning programme for stroke patients: a randomized controlled trial. Clin. Rehabil. 20, 191–200 (2006)

    Google Scholar 

  20. Sharman, M.J., Cresswell, A.G., Riek, S.: Proprioceptive neuromuscular facilitation stretching: mechanisms and clinical implications. Sports Med. 36(11), 929–939 (2006)

    Google Scholar 

  21. Furmanek, M.P., Schettino, L.F., Yarossi, M., Kirkman, S., Adamovich, S.V., Tunik, E.: Coordination of reach-to-grasp in physical and haptic-free virtual environments. J. Neuroeng. Rehabil. 16, 78 (2019)

    Google Scholar 

  22. Harris, J.E., Eng, J.J.: Strength training improves upper-limb function in individuals with stroke a meta-analysis. Stroke. 41(1), 136–140 (2010)

    Google Scholar 

  23. Blennerhassett, J., Dite, W.: Additional task-related practice improves mobility and upper limb function early after stroke: a randomised controlled trial. Aust. J. Physiotherapy. 50, 219–224 (2004)

    Google Scholar 

  24. Dean, C.M., Richards, C.L., Malouin, F.: Task-related circuit training improves performance of locomotor tasks in chronic stroke: a randomized, controlled pilot trial. Arch. Phys. Med. Rehabil. 81, 409–417 (2000)

    Google Scholar 

  25. Malouin, F., Richards, C.L., McFadyen, B., Doyon, J.: New perspectives of locomotor rehabilitation after stroke. Médecine/Sciences. 19(10), 994–998 (2003)

    Google Scholar 

  26. Inman, D.P., Loge, K., Cram, A., Peterson, M.: Learning to drive a wheelchair in virtual reality. J. Spec. Educ. Technol. 26(3), 21–34 (2011)

    Google Scholar 

  27. Wang, B., Shen, M., Wang, Y., He, Z., Chi, S., Yang, Z.: Effect of virtual reality on balance and gait ability in patients with Parkinson’s disease: a systematic review and meta-analysis. Clin. Rehabil. 33(7), 1130–1138 (2019)

    Google Scholar 

  28. Crosbie, J., Lennon, S., McNeill, M., McDonough, S.: Virtual reality in the rehabilitation of the upper limb after stroke: the user’s perspective. CyberPsychol. Behav. 9(2), 137–141 (2006)

    Google Scholar 

  29. Ustinova, K., Perkins, J., Szostakowski, L., Tamkei, L., Leonard, W.: Effect of viewing angle on arm reaching while standing in a virtual environment: potential for virtual rehabilitation. Acta Psychol. 133(2), 180–190 (2010)

    Google Scholar 

  30. Ma, H.I., Hwang, W.J., Wang, C.Y., Fang, J.J., Leong, I.F., Wang, T.Y.: Trunk-arm coordination in reaching for moving targets in people with Parkinson’s disease: comparison between virtual and physical reality. Hum. Mov. Sci. 31, 1340–1352 (2012)

    Google Scholar 

  31. Alamri, A., Eid, M., Iglesias, R., Shirmohammadi, S., El Saddik, A.: Haptic virtual rehabilitation exercises for poststroke diagnosis. IEEE Trans. Instrum. Meas. 57(9), 1876–1884 (2008)

    Google Scholar 

  32. Boian, R., Sharma, A., Han, C., Merians, A.S., Burdea, G.S., Adamovich, S.V., Poizner, H.: Virtual reality-based post-stroke hand rehabilitation. Stud. Health Technol. Inform. 85, 64–70 (2002)

    Google Scholar 

  33. Jack, D., Boian, R., Merians, A.S., Tremaine, M., M., G.S. Burdea, S.V. Adamovich, H.: Poizner: virtual reality-enhanced stroke rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 9(3), 308–318 (2001)

    Google Scholar 

  34. Adamovich, S.V., Merians, A.S., Boian, R., Lewis, J.A., Tremaine, M., Burdea, G.S., Poizner, H.: A virtual reality-based exercise system for hand rehabilitation post-stroke. Presence Teleop. Virt. Environ. 14(2), 161–174 (2005)

    Google Scholar 

  35. Shakra, I., Orozco, M., El Saddik, A., Shirmohammadi, S., Lemaire, E.: VR-based hand rehabilitation using a haptic-based framework. In: Proceedings of the IEEE Conference on Instrumentation and Measurement Technology, Sorrento, pp. 1178–1181 (2006)

    Google Scholar 

  36. Alamri, A., Iglesias, R., Eid, M., El Saddik, A., Shirmohammadi, S., Lemaire, E.: Haptic exercises for measuring improvement of post-stroke rehabilitation patients. In: Proceedings of the IEEE International Workshop on Medical Measurement and Applications, Warsaw, pp. 1–6 (2007)

    Google Scholar 

  37. Lambercy, O., Dovat, L., Gassert, R., Burdet, E., Teo, C.L., Milner, T.: A haptic knob for rehabilitation of hand function. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 356–366 (2007)

    Google Scholar 

  38. Xu, Z., Yu, H., Yan, S.: Motor rehabilitation training after stroke using haptic handwriting and games. In: Proceedings of the 4th International Convention on Rehabilitation Engineering & Assistive Technology, Shanghai, p. 31 (2010)

    Google Scholar 

  39. Gupta, A., O’Malley, M.K.: Design of a haptic arm exoskeleton for training and rehabilitation. IEEE/ASME Trans. Mechatron. 11(3), 280–289 (2006)

    Google Scholar 

  40. Lövquist, F., Dreifaldt, U.: The design of a haptic exercise for post-stroke arm rehabilitation. In: Proceedings of the 6th International Conference on Disability, Virtual Reality and Associate Technologies, Esbjerg, pp. 18–20 (2006)

    Google Scholar 

  41. Song, A., Wu, J., Qin, G., Huang, W.: A novel self-decoupled four degree-of-freedom wrist force/torque sensor. Measurement. 40(9), 883–891 (2007)

    Google Scholar 

  42. Kayyali, R., Alamri, A., Eid, M., Iglesias, R., Shirmohammadi, S., El Saddik, A.: Occupational therapists’ evaluation of haptic motor rehabilitation. In: Proceedings of the 29th Annual International Conference of the IEEE, Lyon, pp. 4763–4766 (2007)

    Google Scholar 

  43. Lo, H.S., **e, S.Q.: Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Med. Eng. Phys. 34(3), 261–268 (2012)

    Google Scholar 

  44. Loureiro, R., Amirabdollahian, F., Top**, M., Driessen, B., Harwin, W.: Upper limb robot mediated stroke therapy – GENTLE/s approach. Auton. Robot. 15(1), 35–51 (2003)

    Google Scholar 

  45. Loureiro, R., Collin, C., Harwin, W.: Robot aided therapy: challenges ahead for upper limb stroke rehabilitation. In: Proceedings of the Fifth International Conference on Disability, Virtual Reality and Associated Technologies, Oxford, UK, pp. 33–39 (2004)

    Google Scholar 

  46. Burdea, G.C.: Virtual rehabilitation-benefits and challenges. Methods Inf. Med. 42(5), 519–523 (2003)

    Google Scholar 

  47. Al-Issa, H., Regenbrecht, H., Hale, L.: Augmented reality applications in rehabilitation to improve physical outcomes. Phys. Ther. Rev. 17(1), 16–28 (2012)

    Google Scholar 

  48. Jaffe, D.L.: Using augmented reality to improve walking in stroke survivors. In: Proceedings of the 12th IEEE International Workshop on Robot and Human Interactive Communication, Millbrae, pp. 79–83 (2003)

    Google Scholar 

  49. Espay, A.J., Baram, Y., Dwivedi, A.K., Shukla, R., Gartner, M., Gaines, L., Revilla, F.J.: At-home training with closed-loop augmented-reality cueing device for improving gait in patients with Parkinson disease. J. Rehabil. Res. Dev. 47, 573–581 (2010)

    Google Scholar 

  50. Luo, X., Kline, T., Fischer, H.C, Stubblefield, K.A., Kenyon, R.V., Kamper, D.G.: Integration of augmented reality and assistive devices for post-stroke hand opening rehabilitation. In: Proceedings of the 27th Annual International Conference of Engineering in Medicine and Biology Society, Shanghai, pp. 6855–6858 (2005)

    Google Scholar 

  51. Lee, R.G., Tien, S.C., Chen, C.C., Chen, Y.Y.: Development of an augmented reality-oriented game system for stroke rehabilitation assessment. Biomed. Eng. Appl. Basis Commun. 24(5), 435–445 (2012)

    Google Scholar 

  52. Wang, H.S., Hsu, C., Chiu, D., Tsai, S.N.: Using augmented reality gaming system to enhance hand rehabilitation. In: Proceedings of the 2nd International Conference on Education Technology and Computer, Shanghai, pp. 243–246 (2010)

    Google Scholar 

  53. Chinthammit, W., Merritt, T., Pedersen, S., Williams, A., Visentin, D., Rowe, R., Furness, T.: Ghostman: augmented reality application for telerehabilitation and remote instruction of a novel motor skill. Biomed. Res. Int. 2014, 1–7 (2014)

    Google Scholar 

  54. Lin, J.K., Cheng, P.H., Su, Y., Wang, S.Y., Lin, H.W., Hou, H.C., Su, M.J.: Augmented reality serious game framework for rehabilitation with personal health records. In: Proceedings of the 13th IEEE International Conference on e-Health Networking Applications and Services, Columbia, pp. 197–200 (2011)

    Google Scholar 

  55. Regenbrecht, H., McGregor, G., Ott, C., Hoermann, S., Schubert, T., Hale, L., Hoermann, J., Dixon, B., Franz, E.: Out of reach? A novel AR interface approach for motor rehabilitation. In: Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality, Basel, pp. 219–228 (2011)

    Google Scholar 

  56. Alamri, A., Cha, J., Eid, M., El Saddik, A.: Evaluating the post-stroke patients progress using an Augmented Reality Rehabilitation system. In: Proceedings of the IEEE International Workshop on Medical Measurements and Applications, Cetraro, pp. 89–94 (2009)

    Google Scholar 

  57. Alamri, A., Cha, J., El Saddik, A.: AR-REHAB: an augmented reality framework for poststroke-patient rehabilitation. IEEE Trans. Instrum. Meas. 59, 2554–2563 (2010)

    Google Scholar 

  58. Lange, B., Chang, C.Y., Suma, E., Newman, B., Rizzo, A.S., Bolas, M.: Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. In: Proceedings of the Annual IEEE International Conference of Engineering in Medicine and Biology Society, Boston, pp. 1831–1834 (2011)

    Google Scholar 

  59. Ponto, K., Kimmel, R., Kohlmann, J., Bartholomew, A., Radwin, R.G.: Virtual exertions: a user interface combining visual information, kinesthetics and biofeedback for virtual object manipulation. In: Proceedings of the IEEE Symposium on 3D User Interfaces (3DUI), Costa Mesa, pp. 85–88 (2012)

    Google Scholar 

  60. Klein, A., Assis, G.A.D.: A markerless Augmented Reality tracking for enhancing the user interaction during virtual rehabilitation. In: Proceedings of the XV Symposium on Virtual and Augmented Reality (SVR), Washington, pp. 117–124 (2013)

    Google Scholar 

  61. Watanabe, Y., Matsutani, A., Niikura, T., Komuro, T., Ishikawa, M.: High-speed estimation of multi-finger position and pose for input interface of the mobile devices. In: Proceedings of the IEEE 1st Global Conference on Consumer Electronics (GCCE), Tokyo, pp. 228–232 (2012)

    Google Scholar 

  62. Metcalf, C.D., Robinson, R., Malpass, A.J., Bogle, T.P., Dell, T.A., Harris, C., Demain, S.H.: Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation. IEEE Trans. Biomed. Eng. 60(8), 2184–2192 (2013)

    Google Scholar 

  63. Fugl-Meyer, A., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand. J. Rehabil. Med. 7(1), 13–31 (1975)

    Google Scholar 

  64. Wolf, S.L., Catlin, P.A., Ellis, M., Archer, A.L., Morgan, B., Piacentino, A.: Assessing Wolf motor function test as outcome measure for research in patients after stroke. Stroke. 32(7), 1635–1639 (2001)

    Google Scholar 

  65. Hsieh, Y.W., Wu, C.Y., Lin, K.C., Chang, Y.F., Chen, C.L., Liu, J.S.: Responsiveness and validity of three outcome measures of motor function after stroke rehabilitation. Stroke. 40(4), 1386–1391 (2009)

    Google Scholar 

  66. McDonnell, M.: Action research arm test. Aust. J. Physiotherapy. 54(3), 220 (2008)

    Google Scholar 

  67. Lyle, R.C.: A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int. J. Rehabil. Res. 4(4), 483–492 (1981)

    Google Scholar 

  68. Yozbatiran, N., Der-Yeghiaian, L., Cramer, S.C.: A standardized approach to performing the action research arm test. Neurorehabil. Neural Repair. 22(1), 78–90 (2008)

    Google Scholar 

  69. Bonato, P.: Advances in wearable technology and applications in physical medicine and rehabilitation. J. Neuroeng. Rehabil. 2(1), 2 (2005)

    Google Scholar 

  70. Williams, N., Penrose, J., Caddy, C., Barnes, E., Hose, D., Harley, P.: A goniometric glove for clinical hand assessment construction, calibration and validation. J. Hand Surg. (Br. Eur. Vol.). 25(2), 200–207 (2000)

    Google Scholar 

  71. Fahn, C.S., Sun, H.: Development of a fingertip glove equipped with magnetic tracking sensors. Sensors. 10(2), 1119–1140 (2010)

    Google Scholar 

  72. Roberts, L., Singhal, G., Kaliki, R.: Slip detection and grip adjustment using optical tracking in prosthetic hands. In: Proceedings of the Annual IEEE International Conference of Engineering in Medicine and Biology Society, Boston, pp. 2929–2932 (2011)

    Google Scholar 

  73. Hermsdörfer, J., Hagl, E., Nowak, D., Marquardt, C.: Grip force control during object manipulation in cerebral stroke. Clin. Neurophysiol. 114(5), 915–929 (2003)

    Google Scholar 

  74. Blennerhassett, J.M., Carey, L.M., Matyas, T.A.: Grip force regulation during pinch grip lifts under somatosensory guidance: comparison between people with stroke and healthy controls. Arch. Phys. Med. Rehabil. 87, 418–429 (2006)

    Google Scholar 

  75. Schaefer, S.Y., DeJong, S.L., Cherry, K.M., Lang, C.E.: Grip type and task goal modify reach-to-grasp performance in post-stroke hemiparesis. Mot. Control. 16(2), 245–264 (2012)

    Google Scholar 

  76. Keil, A., Elbert, T., Taub, E.: Relation of accelerometer and EMG recordings for the measurement of upper extremity movement. J. Psychophysiol. 13(2), 77–82 (1999)

    Google Scholar 

  77. Manson, A., Brown, P., O’Sullivan, J., Asselman, P., Buckwell, D., Lees, A.: An ambulatory dyskinesia monitor. J. Neurol. Neurosurg. Psychiatry. 68(2), 196–201 (2000)

    Google Scholar 

  78. Thielgen, T., Foerster, F., Fuchs, G., Hornig, A., Fahrenberg, J.: Tremor in Parkinson’s disease: 24-hr monitoring with calibrated accelerometry. Electromyogr. Clin. Neurophysiol. 44(3), 137–146 (2004)

    Google Scholar 

  79. Parnandi, A., Wade, E., Mataric, M.: Motor function assessment using wearable inertial sensors. In: Proceedings of the Annual IEEE International Conference of Engineering in Medicine and Biology Society, Buenos Aires, pp. 86–89 (2010)

    Google Scholar 

  80. Uswatte, G., Giuliani, C., Winstein, C., Zeringue, A., Hobbs, L., Wolf, S.L.: Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Arch. Phys. Med. Rehabil. 87(10), 1340–1345 (2006)

    Google Scholar 

  81. Choi, Y.: Ubi-REHAB: An android-based portable Augmented Reality stroke rehabilitation system using the eGlove for multiple participants. In: Proceedings of the International Conference on Virtual Rehabilitation (ICVR), Zurich, pp. 1–2 (2011)

    Google Scholar 

  82. Choi, Y., Gordon, J., Park, H., Schweighofer, N.: Feasibility of the adaptive and automatic presentation of tasks (ADAPT) system for rehabilitation of upper extremity function post-stroke. J. Neuroeng. Rehabil. 8, 42 (2011)

    Google Scholar 

  83. Parsons, T.D., Iyer, A., Cosand, L., Courtney, C., Rizzo, A.A.: Neurocognitive and psychophysiological analysis of human performance within virtual reality environments. Stud. Health Technol. Inform. 142, 247–252 (2009)

    Google Scholar 

  84. Fritz, S.L., Light, K.E., Patterson, T.S., Behrman, A.L., Davis, S.B.: Active finger extension predicts outcomes after constraint-induced movement therapy for individuals with hemiparesis after stroke. Stroke. 36, 1172–1177 (2005)

    Google Scholar 

  85. Mathiowetz, V., Weber, K., Kashman, N., Volland, G.: Adult norms for the nine hole peg test of finger dexterity. Occup. Ther. J. Res. 5, 24–38 (1985)

    Google Scholar 

  86. Hester, T., Hughes, R., Sherrill, D.M., Knorr, B., Akay, M., Stein, J., Bonato, P.: Using wearable sensors to measure motor abilities following stroke. In: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Massachusetts, pp. 4–8 (2006)

    Google Scholar 

  87. Zhao, M.Y., Ong, S.K., Nee, A.Y.C.: Augmented reality-assisted rehabilitation of activities of daily living. In: Proceedings of the 5th International Conference on Internet Technologies & Society, New Taipei City, pp. 89–93 (2014)

    Google Scholar 

  88. Zhang, D., Shen, Y., Ong, S.K., Nee, A.Y.C.: An affordable augmented reality based rehabilitation system for hand motions. In: Proceedings of the International Conference on Cyberworlds, Singapore, pp. 346–353 (2010)

    Google Scholar 

  89. Barnes, M.P.: Rehabilitation after traumatic brain injury. Br. Med. Bull. 55, 927–943 (1999)

    Google Scholar 

  90. Kaber, D., Tupler, L.A., Clamann, M., Gil, G.H., Zhu, B., Swangnetr, M., Jeon, W., Zhang, Y., Qin, X., Ma, W., Lee, Y.S.: Evaluation of an augmented virtual reality and haptic control interface for psychomotor training. Assist. Technol. 26, 51–60 (2014)

    Google Scholar 

  91. Zar, J.H.: Biostatistical Analysis. Prentice Hall, Upper Saddle River (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soh Khim Ong .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ong, S.K., Zhao, M.Y., Nee, A.Y.C. (2023). Augmented Reality-Assisted Healthcare Exercising Systems. In: Nee, A.Y.C., Ong, S.K. (eds) Springer Handbook of Augmented Reality. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-67822-7_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67822-7_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67821-0

  • Online ISBN: 978-3-030-67822-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Navigation