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Hand Exercise Using a Haptic Device

  • Paulo A. S. MendesEmail author
  • João P. Ferreira
  • A. Paulo Coimbra
  • Manuel M. Crisóstomo
  • César Bouças
Conference paper
  • 56 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

It is known that the brain uses the sense of touch, in different parts of the body, to acquire information to react to the environment. With nowadays technology, it is possible to create distinct virtual environments and to feel them with haptic devices. Using haptic devices, it is possible to train and develop different parts of the human body, including the brain. These devices allow users to feel and touch virtual objects with a high realism. The present paper proposes different controller methods to use a haptic device to help the user to exercise their hands. The hand exercises proposed are the straight-line, square, circle and ellipse follow-up. In this work four different types of controllers are compared: proportional, proportional-derivative and logarithmic and sigmoid function based controllers. Each one of the used controllers were tested with the hand exercises mentioned. The sigmoid and logarithmic function based controllers achieves more suitable results for the user haptic perception and trajectory follow-up.

Keywords

Haptic device Digital control Hand rehabilitation 

References

  1. 1.
    Aijaz, A., Dohler, M., Aghvami, A.H., Friderikos, V., Frodigh, M.: Realizing the tactile internet: haptic communications over next generation 5G cellular networks. IEEE Wirel. Commun. 24(2), 82–89 (2016)CrossRefGoogle Scholar
  2. 2.
    Beckman, J.A.: The PHANTOM Omni as an under-actuated robot (2007)Google Scholar
  3. 3.
    Brewster, S.: Impact of haptic ‘touching’ technology on cultural applications (2005)Google Scholar
  4. 4.
    Dandu, B., Shao, Y., Stanley, A., Visell, Y.: Spatiotemporal haptic effects from a single actuator via spectral control of cutaneous wave propagation, pp. 425–430 (2019)Google Scholar
  5. 5.
    Dhiab, A.B., Hudin, C.: Confinement of vibrotactile stimuli in narrow plates. In: 2019 IEEE World Haptics Conference (WHC), pp. 431–436. IEEE (2019)Google Scholar
  6. 6.
    Gao, Y., Hendricks, L.A., Kuchenbecker, K.J., Darrell, T.: Deep learning for tactile understanding from visual and haptic data. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 536–543. IEEE (2016)Google Scholar
  7. 7.
    Grigorii, R., Peshkin, M., Colgate, J.: Stiction rendering in touch, pp. 13–18 (2019)Google Scholar
  8. 8.
    Hannaford, B., Ryu, J.-H.: Time-domain passivity control of haptic interfaces. IEEE Trans. Robot. Autom. 18(1), 1–10 (2002)CrossRefGoogle Scholar
  9. 9.
    Kim, Y.K., Yang, X.: Hand-writing rehabilitation in the haptic virtual environment. In: 2006 IEEE International Workshop on Haptic Audio Visual Environments and their Applications (HAVE 2006), pp. 161–164, November 2006Google Scholar
  10. 10.
    Kong, R., Dong, X., Liu, X.: Position and force control of teleoperation system based on PHANTOM Omni robots. Int. J. Mech. Eng. Robot. Res. 5(1), 57 (2016)Google Scholar
  11. 11.
    Koul, M., Kumar, P., Singh, P., Muniyandi, M., Saha, S.: Gravity compensation for PHANTOM Omni haptic interface, May 2010Google Scholar
  12. 12.
    Martín, J.S., Triviño, G.: A study of the manipulability of the PHANToM OMNI haptic interface (2006)Google Scholar
  13. 13.
    Martinez, M.O., et al.: 3-D printed haptic devices for educational applications. In: 2016 IEEE Haptics Symposium (HAPTICS), pp. 126–133. IEEE (2016)Google Scholar
  14. 14.
    Mohammadi, A., Tavakoli, M., Jazayeri, A.: Phansim: a simulink toolkit for the sensable phantom haptic devices. In: Proceedings of the 23rd CANCAM, Canada, vol. 11, pp. 787–790 (2011)Google Scholar
  15. 15.
    Quaid, A., et al.: Haptic guidance system and method. US Patent App. 10/231,790, 19 March 2019Google Scholar
  16. 16.
    Sebastian, F., Fu, Q., Santello, M., Polygerinos, P.: Soft robotic haptic interface with variable stiffness for rehabilitation of neurologically impaired hand function. Front. Robot. AI 4, 69 (2017). https://www.frontiersin.org/article/10.3389/frobt.2017.00069CrossRefGoogle Scholar
  17. 17.
    Seim, C., et al.: Towards haptic learning on a smartwatch. In: Proceedings of the 2018 ACM International Symposium on Wearable Computers, pp. 228–229. ACM (2018)Google Scholar
  18. 18.
    Srimathveeravalli, G., Gourishankar, V., Kesavadas, T.: Comparative study: virtual fixtures and shared control for rehabilitation of fine motor skills. In: Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC 2007), pp. 304–309, March 2007Google Scholar
  19. 19.
    Srimathveeravalli, G., Kesavadas, T.: Motor skill training assistance using haptic attributes, pp. 452–457, April 2005Google Scholar
  20. 20.
    Vardar, Y.: Tactile perception by electrovibration. Ph.D. thesis, Koç University (2018)Google Scholar
  21. 21.
    Vetter, J.: Tangible signals-physical representation of sound and haptic control feedback. In: Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction, pp. 741–744. ACM (2019)Google Scholar
  22. 22.
    Xu, X., Steinbach, E.: Elimination of cross-dimensional artifacts in the multi-dof time domain passivity approach for time-delayed teleoperation with haptic feedback, pp. 223–228 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Paulo A. S. Mendes
    • 1
    Email author
  • João P. Ferreira
    • 1
    • 2
  • A. Paulo Coimbra
    • 1
    • 3
  • Manuel M. Crisóstomo
    • 1
    • 3
  • César Bouças
    • 1
  1. 1.ISR - Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal
  2. 2.Department of Electrical EngineeringSuperior Institute of Engineering of CoimbraCoimbraPortugal
  3. 3.Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal

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