Skip to main content
SpringerLink
Log in

Improving Motivation in Wrist Rehabilitation Therapies

  • Conference paper
  • First Online:
Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence (ISAmI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1006 ))

Included in the following conference series:

  • 647 Accesses

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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. Cialdini, R.B.: Influence: The Psychology of Persuation. Morrow, New York (1993)

    Google Scholar 

  8. Cram, J.R., Steger, J.C.: EMG scanning in the diagnosis of chronic pain. Biofeedback Self Regul. 8(2), 229–241 (1983)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Holden, M.K.: Virtual environments for motor rehabilitation: review. CyberPsychology Behav. 8(3), 187–211 (2005)

    Article  Google Scholar 

  14. Horger, M.M.: The reliability of goniometric measurements of active and passive wrist motions. Am. J. Occup. Ther. 44(4), 342–348 (1990)

    Article  Google Scholar 

  15. Kingston, B.: Understanding Joints: A Practical Guide to Their Structure and Function. Nelson Thornes (2000)

    Google Scholar 

  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. Leap Motion Inc: Leap Motion. https://www.leapmotion.com/

  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)

    Article  Google Scholar 

  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. 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. World Health Organization: International Classification of Functioning, Disability and Health: ICF. World Health Organization (2001)

    Google Scholar 

  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. 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. 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. Skirven, T.M., Osterman, A.L., Fedorczyk, J.M., Amadio, P.C.: Rehabilitation of the Hand and Upper Extremity. Mosby (2011)

    Google Scholar 

  26. Slutsky, D.J., Herman, M.: Rehabilitation of distal radius fractures: a biomechanical guide. Hand Clin. 21(3), 455–468 (2005)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  29. Thalmic Labs Inc.: Myo Gesture Control Armband

    Google Scholar 

  30. Vines, A.: Helping your wrist to recover after a fracture. Oxford University Hospitals NHS Trust (2015)

    Google Scholar 

  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)

    Article  Google Scholar 

  32. Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Signal Process. Control 3(1), 1–18 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by Spanish Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under Vi-SMARt (TIN2016-79100-R).

Author information

Authors and Affiliations

  1. LoUISE Research Group, Computing Systems, University of Castilla-La Mancha, Albacete, Spain

    Miguel A. Teruel, Víctor López-Jaquero, Miguel A. Sánchez-Cifo, Elena Navarro & Pascual González

Authors
  1. Miguel A. Teruel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Víctor López-Jaquero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel A. Sánchez-Cifo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elena Navarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pascual González
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elena Navarro .

Editor information

Editors and Affiliations

  1. Departamento de Informática, Universidade do Minho, Braga, Portugal

    Paulo Novais

  2. Department of Communications, Polytechnic University of Valencia, Valencia, Valencia, Spain

    Jaime Lloret

  3. IoT European Digital Innovation Hub, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  4. Departamento de Informática, Universidade do Minho, Braga, Portugal

    Davide Carneiro

  5. Escuela de Ingenieros Industriales de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain

    Elena Navarro

  6. Faculty of Robotics and Design Engineering, Osaka Institute of Technology, Osaka, Japan

    Sigeru Omatu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Teruel, M.A., López-Jaquero, V., Sánchez-Cifo, M.A., Navarro, E., González, P. (2020). Improving Motivation in Wrist Rehabilitation Therapies. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds) Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence. ISAmI 2019. Advances in Intelligent Systems and Computing, vol 1006 . Springer, Cham. https://doi.org/10.1007/978-3-030-24097-4_24

Download citation

Publish with us

Policies and ethics

Search

Navigation