Skip to main content
SpringerLink
Log in

Augmented reality-based training system for hand rehabilitation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This study designs a training system for hand rehabilitation on the basis of augmented reality technology, which enables patients to simultaneously interact with real and virtual environments. The system framework is introduced, and four rehabilitation programs, namely, trajectory training, shelf training, batting training, and spile training, are presented. As a requirement of hand rehabilitation training, a color marker that is suitable for hand rehabilitation training is adopted. Following the Hamming coding principle, this marker is designed as a 7 × 7 square that is filled up by four designated colors with a binary bit of “0” or “1”. The check code in each row of the color marker is applied to restore the occluded binary bits, solve the occlusion issue of color markers, and complete the tracking registration of the color markers. The effectiveness of the developed system is evaluated via a usability study and questionnaires. The evaluation provides positive results. Therefore, the developed system has potential as an effective rehabilitation system for upper limb impairment.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Alamri A, Cha J, Saddik AE (2010) AR-REHAB: an augmented reality framework for poststroke-patient rehabilitation. IEEE Trans Instrum Meas 59(10):2554–2563

    Article  Google Scholar 

  2. Alamri A, Heung-Nam K, Saddik AE (2010) A decision model of stroke patient rehabilitation with augmented reality-based games. Proc Int Conf Autonomous Intell Syst, 1–6

  3. Aung YM, Al-Jumaily A (2011) Rehabilitation exercise with real-time muscle simulation based EMG and AR. Proc Int Conf Hybrid Intell Syst, 641–646

  4. Aung YM, Al-Jumaily A (2012) AR based upper limb rehabilitation system. Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron, 213–218

  5. Aung YM, Al-Jumaily A, Anam K (2014) A novel upper limb rehabilitation system with self-driven virtual arm illusion. Proc Int Conf IEEE Eng Med Biol Soc, 3614–3617

  6. Burke JW, McNeill MDJ, Charles DK, Morrow PJ (2010) Augmented reality games for upper-limb stroke rehabilitation. Proc IEEE Int Conf Games Virtual Worlds Serious Appl, 75–78

  7. Carbonaro N, Mura GD, Lorussi F et al (2014) Exploiting wearable goniometer technology for motion sensing gloves. IEEE J Biomed Health Inform 18(6):1788–1795

    Article  Google Scholar 

  8. Choi Y (2011) Ubi-REHAB: an android-based portable augmented reality stroke rehabilitation system using the eGlove for multiple participants. Proc IEEE Int Conf Virtual Rehab (ICVR), 1–2

  9. Collins J, Hoermann S, Regenbrecht H (2014) Virtualising the nine hole peg test of finger dexterity. Proc 10th Int Conf Disability, Virtual Reality Assoc Technol, 181–188

  10. Cramer SC, Sur M, Dobkin BH et al (2011) Harnessing neuroplasticity for clinical applications. Brain 134(6):1591–1609

    Article  Google Scholar 

  11. Hoermann S, Hale L, Winser SJ, Regenbrecht H (2012) Augmented reflection technology for stroke rehabilitation–A clinical feasibility study. Proc Int Conf Disability, Virtual Reality Assoc Technol, 1–9

  12. Holden MK (2005) Virtual environments for motor rehabilitation: review. Cyberpsychol Behav 8(3):187–211

    Article  Google Scholar 

  13. Hondor HM, Khademi M, Dodakian L, Cramer SC, Lopes CV (2013) A spatial augmented reality rehab system for post-stroke hand rehabilitation. Stud Health Technol Inform 184:279–285

    Google Scholar 

  14. Langhorne P, Coupar F, Pollock A (2009) Motor recovery after stroke: a systematic review. Lancet Neurol 8(8):741–754

    Article  Google Scholar 

  15. Luo X, Kenyon RV, Kline T, Waldinger HC, Kamper DG (2005) An augmented reality training environment for post-stroke finger extension rehabilitation. Proc IEEE 9th Int Conf Rehab Robot, 329–332

  16. Luo X, Kline T, Fischer H et al (2005) Integration of augmented reality and assistive devices for post-stroke hand opening rehabilitation. Proc IEEE Int Conf Eng Med Biol Soc 7:6855–6858

    Google Scholar 

  17. Mathiowetz V, Weber K, Kashman N, Volland G (1985) Adult norms for the nine hole peg test of finger dexterity. OTJR 5(1):24–38

    Google Scholar 

  18. Ong SK, Shen Y, Zhang J, Nee AYC (2011) Augmented reality in assistive technology and rehabilitation engineering. In: Furht B (ed) Handbook of Augmented Reality. Springer, New York, pp 603–630

  19. Pons TP, Garraghty PE, Ommaya AK et al (1991) Massive cortical reorgazation after sensory deafferentation in adult macaques. Science 252:1857–1860

    Article  Google Scholar 

  20. Regenbrecht H, McGregor G, Ott C, Mueller L, Franz E (2014) Manipulating the experience of reality for rehabilitation applications. Proc IEEE 102(102):170–184

    Article  Google Scholar 

  21. Regenbrecht H, McGregor G, Ott C, Hoermann S (2011) Out of reach? a novel AR interface approach for motor rehabilitation. Proc IEEE Int Symp Mixed Augmented Reality (ISMAR), 219–228

  22. Shen Y, Ong SK, Nee AYC (2009) Hand rehabilitation based on augmented reality. Proc ACM 3rd Int Convention Rehab Eng Assistive Technol, 1–4

  23. Sucar LE, Leder RS, Reinkensmeyer D, Hernandez J, Azcarate G (2008) Gesture therapy: a low-cost vision-based system for rehabilitation after stroke. Proc AMC 1st Int Conf Health Inform, 107–111

  24. Takeuchi N, Izumi SI (2013) Rehabilitation with poststroke motor recovery: a review with a focus on neural plasticity. Stroke Res Treatment 2013

  25. Zhang D, Shen Y, Ong SK, Nee AYC (2010) An affordable augmented reality based rehabilitation system for hand motions. Proc IEEE Int Conf Cyberworlds 7(8):346–353

    Google Scholar 

  26. Zhou JM, Huang JW, Lao J et al (2012) The clinical utility of hand function rehabilitation science. Shanghai World Book Publishing Company, Shanghai, pp 8–14

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61203316, 61203319, 61502240), the Natural Science Foundation of Jiangsu Province (BK20141002), Jiangsu Government Scholarship for Overseas Studies, and the Jiangsu Students’ Project for Innovation and Entrepreneurship Training Program (No. 201510300090).

Author information

Authors and Affiliations

  1. B-DAT & CICAEET, School of Information and Control, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Jia Liu, Jianhui Mei, Xiaorui Zhang & Jing Huang

  2. School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Xiong Lu

Authors
  1. Jia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhui Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaorui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jia Liu.

Appendix A

Appendix A

The scoring rules and scoring items in the questionnaire are as follows:

Scoring Rules: Every subject needs to score once he /she completed the test (0–10 points). The full mark is 10 points. The average score and standard deviation for each item are obtained at the end of all tests.

Scoring Items:

I1. Easy to understand

I2. Clear instructions

I3. Enjoyable

I4. AR environment

I5. Interaction

I6. Operability

I7. Motivating

I8. Portable

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Mei, J., Zhang, X. et al. Augmented reality-based training system for hand rehabilitation. Multimed Tools Appl 76, 14847–14867 (2017). https://doi.org/10.1007/s11042-016-4067-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4067-x

Keywords

Search

Navigation