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Wrist Motor Function Rehabilitation Training and Evaluation System Based on Human-Computer Interaction

  • Haichuan Ren
  • Qi Song
  • Yanhong LiuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)

Abstract

Based on human-computer interaction, a wrist motor function rehabilitation training and evaluation system is developed for the treatment or improvement of wrist motor dysfunction. Specifically, the joint angle sensor and the MYO wristband are used to realize the perception of the wrist motion on the ROS, the wrist motor function rehabilitation training game with information feedback is designed, and the quantitative evaluation on the wrist motor function is realized. The experimental results demonstrate that in the rehabilitation training session, the online accuracy of wrist motion recognition is 95.2%, and in the evaluation session, the root mean square error of the measured and actual values of the wrist joint angle is less than 5°. The paper works provide the basis for further clinical experiments of the wrist motor function rehabilitation training and evaluation.

Keywords

Human-computer interaction ROS Wrist motor function evaluation Rehabilitation training 

References

  1. 1.
    Pandian, S., Arya, K.N., Davidson, E.W.R.: Comparison of Brunnstrom movement therapy and motor relearning program in rehabilitation of post-stroke hemiparetic hand: a randomized trial. J. Bodywork Mov. Ther. 16(03), 330–337 (2012)CrossRefGoogle Scholar
  2. 2.
    Serrien, D.J., Strens, L.H., Cassidy, M.J., et al.: Functional significance of the ipsilateral hemisphere during movement of the affected hand after stroke. Exp. Neurol. 190(02), 425–432 (2004)CrossRefGoogle Scholar
  3. 3.
    Tsoupikova, D., Stoykov, N.S., Corrigan, M., et al.: Virtual immersion for post-stroke hand rehabilitation therapy. Ann. Biomed. Eng. 43(02), 467–477 (2015)CrossRefGoogle Scholar
  4. 4.
    Hasani, F.N., MacDermid, J.C., Tang, A., Kho, M.E.: Cross-cultural adaptation and psychometric testing of the Arabic version of the Patient-Rated Wrist Hand Evaluation (PRWHE-A) in Saudi Arabia. J. Hand Ther. 28(4), 412–420 (2015)CrossRefGoogle Scholar
  5. 5.
    Kennedy, S.A., Stoll, L.E., Lauder, A.S.: Human and other mammalian bite injuries of the hand: evaluation and management. J. Am. Acad. Orthop. Surg. 23(1), 47–57 (2015)CrossRefGoogle Scholar
  6. 6.
    Thielbar, K.O., Lord, T.J., Fischer, H.C., et al.: Training finger individuation with a mechatronic-virtual reality system leads to improved fine motor control post-stroke. J. Neuroengineering Rehabil. 11(01), 171 (2014)CrossRefGoogle Scholar
  7. 7.
    Rivas, J.J., Heyer, P., et al.: Towards incorporating affective computing to virtual rehabilitation; surrogating attributed attention from posture for boosting therapy adaptation. In: International Symposium on Medical Information Processing and Analysis, vol. 92(87), 58–63 (2015)Google Scholar
  8. 8.
    Heuser, A., Kourtev, H., Hentz, V., et al.: Tele-rehabilitation using the Rutgers Master II glove following Carpal Tunnel Release surgery. In: International Workshop on Virtual Rehabilitation, vol. 15(01), pp. 88–93 (2007)Google Scholar
  9. 9.
    Sucar, L.E., Orihuela, E.F., Velazquez, R.L., et al.: Gesture therapy: an upper limb virtual reality-based motor rehabilitation platform. IEEE Trans. Neural Syst. Rehabil. Eng. 22(03), 634–643 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina

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