Posts Tagged MYO

[Abstract + References] Wrist Motor Function Rehabilitation Training and Evaluation System Based on Human-Computer Interaction – Conference paper


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.


  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

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[Abstract + References] Improving Motivation in Wrist Rehabilitation Therapies – Conference paper


Rehabilitation encompasses a wide variety of activities aimed at reducing the impact of injuries and disabilities by applying different exercises. Frequently, such exercises are carried out at home as a repetition of the same movements or tasks to achieve both motor learning and the necessary cortical changes. Although this increases the patients’ available time for rehabilitation, it may also have some unpleasant side effects. That occurs because carrying out repetitive exercises in a more isolated environment may result in a boring activity that leads patients to give up their rehabilitation. Therefore, patients’ motivation should be considered an essential feature while designing rehabilitation exercises. In this paper, we present how we have faced this need by exploiting novel technology to guide patients in their rehabilitation process. It includes a game crafted to make recovery funny and useful, at the same time. The game and the use we made of the specific hardware follow the recommendations and good practices provided by medical experts.


  1. 1.
    Aguiar, L.F., Bo, A.P.L.: Hand gestures recognition using electromyography for bilateral upper limb rehabilitation. In: 2017 IEEE Life Sciences Conference (LSC), pp. 63–66. IEEE (2017)Google Scholar
  2. 2.
    Amirabdollahian, F., Walters, M.L.: Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 111–115 (2017)Google Scholar
  3. 3.
    Batista, T.V.V., Machado, L.S., Valenca, A.M.G.: Surface electromyography for game-based hand motor rehabilitation. In: 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), pp. 140–144. IEEE (2016)Google Scholar
  4. 4.
    Bevilacqua, V., Brunetti, A., Trigiante, G., Trotta, G.F., Fiorentino, M., Manghisi, V., Uva, A.E.: Design and Development of a Forearm Rehabilitation System Based on an Augmented Reality Serious Game. Presented at the (2016)Google Scholar
  5. 5.
    Bütefisch, C., Hummelsheim, H., Denzler, P., Mauritz, K.H.: Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J. Neurol. Sci. 130(1), 59–68 (1995)CrossRefGoogle Scholar
  6. 6.
    Charles, S.K., Krebs, H.I., Volpe, B.T., Lynch, D., Hogan, N.: Wrist rehabilitation following stroke: initial clinical results. In: Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, pp. 13–16. IEEE (2005)Google Scholar
  7. 7.
    Cialdini, R.B.: Influence: The Psychology of Persuation. Morrow, New York (1993)Google Scholar
  8. 8.
    Cram, J.R., Steger, J.C.: EMG scanning in the diagnosis of chronic pain. Biofeedback Self Regul. 8(2), 229–241 (1983)CrossRefGoogle Scholar
  9. 9.
    Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification using game-design elements in non-gaming contexts. In: 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA 2011), pp. 24–25. ACM Press, Vancouver (2011)Google Scholar
  10. 10.
    Dromerick, A.W., Edwards, D.F., Hahn, M.: Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke? Stroke 31(12), 2984–2988 (2000)CrossRefGoogle Scholar
  11. 11.
    Esfahlani, S.S., Thompson, T., Parsa, A.D., Brown, I., Cirstea, S.: ReHabgame: a non-immersive virtual reality rehabilitation system with applications in neuroscience. Heliyon 4(2), e00526 (2018)CrossRefGoogle Scholar
  12. 12.
    He, S., Yang, C., Wang, M., Cheng, L., Hu, Z.: Hand gesture recognition using MYO armband. Chinese Automation Congress (CAC), 2017, pp. 4850–4855 (2017)Google Scholar
  13. 13.
    Holden, M.K.: Virtual environments for motor rehabilitation: review. CyberPsychology Behav. 8(3), 187–211 (2005)CrossRefGoogle Scholar
  14. 14.
    Horger, M.M.: The reliability of goniometric measurements of active and passive wrist motions. Am. J. Occup. Ther. 44(4), 342–348 (1990)CrossRefGoogle Scholar
  15. 15.
    Kingston, B.: Understanding Joints: A Practical Guide to Their Structure and Function. Nelson Thornes (2000)Google Scholar
  16. 16.
    Langan, J., Subryan, H., Nwogu, I., Cavuoto, L.: Reported use of technology in stroke rehabilitation by physical and occupational therapists. Disabil. Rehabil. Assist. Technol. 13(7), 1–7 (2017)Google Scholar
  17. 17.
    Leap Motion Inc: Leap Motion.
  18. 18.
    Van der Lee, J.H., Wagenaar, R.C., Lankhorst, G.J., Vogelaar, T.W., Devillé, W.L., Bouter, L.M.: Forced use of the upper extremity in chronic stroke patients: results from a single-blind randomized clinical trial. Stroke 30(11), 2369–2375 (1999)CrossRefGoogle Scholar
  19. 19.
    López-Jaquero, V., Montero, F., Teruel, M.A.: Influence awareness: considering motivation in computer-assisted rehabilitation. J. Ambient Intell. Humaniz. Comput. 10(6), 2018–2197 (2017)Google Scholar
  20. 20.
    Mendez, I., Hansen, B.W., Grabow, C.M., Smedegaard, E.J.L., Skogberg, N.B., Uth, X.J., Bruhn, A., Geng, B., Kamavuako, E.N.: Evaluation of the Myo armband for the classification of hand motions. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 1211–1214 (2017)Google Scholar
  21. 21.
    World Health Organization: International Classification of Functioning, Disability and Health: ICF. World Health Organization (2001)Google Scholar
  22. 22.
    Ortiz-Catalan, M., Nijenhuis, S., Ambrosch, K., Bovend’Eerdt, T., Koenig, S., Lange, B.: Virtual reality. In: Emerging Therapies in Neurorehabilitation, pp. 249–265. Springer (2014)Google Scholar
  23. 23.
    Rechy-Ramirez, E.J., Marin-Hernandez, A., Rios-Figueroa, H.V.: A human-computer interface for wrist rehabilitation: a pilot study using commercial sensors to detect wrist movements. Vis. Comput., 1–15 (2017)Google Scholar
  24. 24.
    Sathiyanarayanan, M., Rajan, S.: MYO Armband for physiotherapy healthcare: A case study using gesture recognition application. In: 2016 8th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–6 (2016)Google Scholar
  25. 25.
    Skirven, T.M., Osterman, A.L., Fedorczyk, J.M., Amadio, P.C.: Rehabilitation of the Hand and Upper Extremity. Mosby (2011)Google Scholar
  26. 26.
    Slutsky, D.J., Herman, M.: Rehabilitation of distal radius fractures: a biomechanical guide. Hand Clin. 21(3), 455–468 (2005)CrossRefGoogle Scholar
  27. 27.
    Tabor, A., Bateman, S., Scheme, E., Flatla, D.R., Gerling, K.: Designing game-based myoelectric prosthesis training. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems – CHI 2017, pp. 1352–1363. ACM Press, New York (2017)Google Scholar
  28. 28.
    Teruel, M.A., Navarro, E., González, P., López-Jaquero, V., Montero, F.: Applying thematic analysis to define an awareness interpretation for collaborative computer games. Inf. Softw. Technol. 74, 17–44 (2016)CrossRefGoogle Scholar
  29. 29.
    Thalmic Labs Inc.: Myo Gesture Control ArmbandGoogle Scholar
  30. 30.
    Vines, A.: Helping your wrist to recover after a fracture. Oxford University Hospitals NHS Trust (2015)Google Scholar
  31. 31.
    Wolf, S.L., Winstein, C.J., Miller, J.P., Taub, E., Uswatte, G., Morris, D., Giuliani, C., Light, K.E., Nichols-Larsen, D.: EXCITE investigators, for the: effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. J. Am. Med. Assoc. 296(17), 2095–2104 (2006)CrossRefGoogle Scholar
  32. 32.
    Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Signal Process. Control 3(1), 1–18 (2008)CrossRefGoogle Scholar

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[Abstract] An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation


Rehabiliation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.

Source: An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation – IEEE Xplore Document

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[Poster] Utility and Usability of the MYO Gesture Armband as a Fine Motor Virtual Reality Gaming Intervention

To establish utility and usability of the MYO Gesture Armband (MYO) as a controller for playing virtual reality (VR) games as a tool for hand motor rehabilitation.

Source: Utility and Usability of the MYO Gesture Armband as a Fine Motor Virtual Reality Gaming Intervention – Archives of Physical Medicine and Rehabilitation

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