Interactive System Using Myoelectric Muscle Sensors for the Strengthening Upper Limbs in Children

  • Victoria M. LópezEmail author
  • Pablo A. ZambranoEmail author
  • Marco PilatasigEmail author
  • Franklin M. SilvaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10851)


This work presents a system for strengthening upper limbs in children through an interactive videogame system and the use of myoelectric muscle sensors. The system allows the acquisition of myoelectric signals taken by electrodes placed in the muscles of interest so they are sent to the computer to be visualized in the virtual interface. Several virtual interfaces were developed in the Unity 3D graphical engine in which the degree of difficulty of the videogame can be selected as well as the muscle affectation and the duration of the repetitions of each exercise. User personal data is stored in a data sheet. The data transmission is carried out using Bluetooth wireless technology in charge of establishing a reliable and real-time communication. Tests were performed on 5 users (3 boys and 2 girls) with ages between 6 to 12 years, and the SUS usability test was applied with results (84.5 ± 0.62), which allows to determine that the system has a good acceptance to be used in muscle strengthening.


Muscle strengthening Interactive system Myoelectric muscle sensor Unity 3D 



We thank the “Universidad de las Fuerzas Armadas ESPE” for financing the investigation project number 2016-PIC-0017.


  1. 1.
    World Health Organization: World report on disability (2011)Google Scholar
  2. 2.
    Feldman, H.M., Chaves-Gnecco, D., Hofkosh, D.: Developmental-behavioral pediatrics. In: Zitelli, B.J., McIntire, S.C., Norwalk, A.J. (eds.) Atlas of Pediatric Diagnosis, 6th edn, Chap. 3. Elsevier Saunders, Philadelphia (2012)Google Scholar
  3. 3.
    Newell, K.: Constraints on the development of coordination. In: Wade, M.G., Whiting, H.T. (eds.) Motor Development in Children: Aspects of Coordination and Control. Nijhoff, Dordrecht (1986)Google Scholar
  4. 4.
    Kakebeeke, T.H., Lanzi, S., Zysset, A.E., Arhab, A., Messerli-Bürgy, N., Stuelb, K., Munsch, S.: Association between body composition and motor performance in preschool children. Obes. Facts 10(5), 420–431 (2017)CrossRefGoogle Scholar
  5. 5.
    Albiol-Pérez, S., Gómez, J.-A.G., Olmo, E., Soler, A.M.: A virtual fine rehabilitation system for children with cerebral palsy: assesment of the usability of a low-cost system. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2017. AISC, vol. 570, pp. 619–627. Springer, Cham (2017). Scholar
  6. 6.
    Booth, V., Masud, T., Connell, L., Bath-Hextall, F.: The effectiveness of virtual reality interventions in improving balance in adults with impaired balance compared with standard or no treatment: a systematic review and meta-analysis. Clin. Rehabil. 28, 419–431 (2014)CrossRefGoogle Scholar
  7. 7.
    Bonnechère, B.: Serious Games in Physical Rehabilitation: From Theory to Practice. Springer, Brussels (2017). Scholar
  8. 8.
    Atashzar, S.F., Shahbazi, M., Samotus, O., Tavakoli, M., Jog, M.S., Patel, R.V.: Characterization of upper-limb pathological tremors: application to design of an augmented haptic rehabilitation system. IEEE J. Sel. Top. Sign. Proces. 10(5), 888–903 (2016)CrossRefGoogle Scholar
  9. 9.
    Jiang, T.T., Qian, Z.Q., Lin, Y., Bi, Z.M., Liu, Y.F., Zhang, W.J.: Analysis of virtual environment haptic robotic systems for a rehabilitation of post-stroke patients. In: 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 738–742. IEEE, Toronto (2017)Google Scholar
  10. 10.
    Andaluz, V.H., et al.: Virtual reality integration with force feedback in upper limb rehabilitation. In: Bebis, G., Boyle, R. (eds.) ISVC 2016. LNCS, vol. 10073, pp. 259–268. Springer, Cham (2016). Scholar
  11. 11.
    Andaluz, V.H., et al.: Virtual environments for motor fine skills rehabilitation with force feedback. In: De Paolis, L.T., Bourdot, P., Mongelli, A. (eds.) AVR 2017. LNCS, vol. 10324, pp. 94–105. Springer, Cham (2017). Scholar
  12. 12.
    Kim, W.-S.: Development and Validation of Assessment Tools Using Robotic and Virtual Reality Technologies in Stroke Rehabilitation, Seoul, August 2016Google Scholar
  13. 13.
    Berger, D.J., d’Avella, A.: Towards a myoelectrically controlled virtual reality interface for synergy-based stroke rehabilitation. In: Ibáñez, J., González-Vargas, J., Azorín, J., Akay, M., Pons, J. (eds.) Converging Clinical and Engineering Research on Neurorehabilitation II. BIOSYSROB, vol. 15, pp. 965–969. Springer, Cham (2017). Scholar
  14. 14.
    Bolgla, L.A., Cruz, M.F., Roberts, L.H., Buice, A.M., Pou, T.S.: Relative electromyographic activity in trunk, hip, and knee muscles during unilateral weight bearing exercises: implications for rehabilitation. Physiother. Theor. Pract. 32, 130–138 (2016)CrossRefGoogle Scholar
  15. 15.
    Dorsch, S., Ada, L., Canning, C.G.: EMG-triggered electrical stimulation is a feasible intervention to apply to multiple arm muscles in people early after stroke, but does not improve strength and activity more than usual therapy: a randomized feasibility trial. Clin. Rehabil. 28, 482–490 (2014)CrossRefGoogle Scholar
  16. 16.
    Calabrò, R.S., Naro, A., Russo, M., Leo, A., Luca, R.D.: The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial. J. NeuroEng. Rehabil. 14, 53 (2017)CrossRefGoogle Scholar
  17. 17.
    Blana, D., Kyriacou, T., Lambrecht, J.M., Chadwick, E.K.: Feasibility of using combined EMG and kinematic signals for prosthesis control: a simulation study using a virtual reality environment. J. Electromyogr. Kinesiol. 29, 21–27 (2016)CrossRefGoogle Scholar
  18. 18.
    Hoda, M., Hafidh, B., El Saddik, A.: Haptic glove for finger rehabilitation. In: 2015 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Turin, pp. 1–6 (2015)Google Scholar
  19. 19.
    Connelly, L., Jia, Y., Toro, M.L., Stoykov, M.E., Kenyon, R.V., Kamper, D.G.: A pneumatic glove and immersive virtual reality environment for hand rehabilitative training after stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 18(5), 551–559 (2010)CrossRefGoogle Scholar
  20. 20.
    Gunel, M.K., Kara, O.K., Ozal, C., Turker, D.: Virtual Reality in Rehabilitation of Children with cerebral palsy. INTECH, Ankara (2014)Google Scholar
  21. 21.
    Won, A.S., Bailey, J., Bailenson, J., Tataru, C., Yoon, I.A.: Immersive Virtual Reality for Pediatric Pain, Children (2017)Google Scholar
  22. 22.
    Garner, T.: Applications of virtual reality. In: Echoes of Other Worlds: Sound in Virtual Reality, pp. 299–361 (2018)Google Scholar
  23. 23.
    Lemmens, R.J.M., Seelen, H.A.M., Timmermans, A.A.A., Schnackers, M.L.A.P., Eerden, A., Smeets, R.J.E.M., Janssen-Potten, Y.J.M.: To what extent can arm–hand skill performance—of both healthy adults and children—be recorded reliably using multiple bodily worn sensor devices? IEEE Trans. Neural Syst. Rehabil. Eng. 23(4), 581–590 (2015)CrossRefGoogle Scholar
  24. 24.
    Wen, X., Duan, F., Yu, Y., Tan, J.T.C., Cheng, X.: Design of a multi-functional system based on virtual reality for stroke rehabilitation. In: 11th World Congress on Intelligent Control and Automation, pp. 2412–2417. IEEE, Shenyang (2014)Google Scholar
  25. 25.
    Sharfina, Z., Santoso, H.B.: An Indonesian adaptation of the System Usability Scale (SUS). In: 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 145–148. IEEE, Malang (2016)Google Scholar
  26. 26.
    Jannink, M.J., Van Der Wilden, G.J., Navis, D.W., Visser, G., Gussinklo, J., Ijzerman, M.: A low-cost video game applied for training of upper extremity function in children with cerebral palsy: a pilot study. Cyberpsychol. Behav. 11(1), 27–32 (2008)CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad de las Fuerzas Armadas ESPESangolquíEcuador

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