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Assistive Hand Exoskeletons: The Prototypes Evolution at the University of Florence

  • Nicola SeccianiEmail author
  • Matteo Bianchi
  • Alessia Meschini
  • Alessandro Ridolfi
  • Yary Volpe
  • Lapo Governi
  • Benedetto Allotta
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 68)

Abstract

Robotic assistance to hand-impaired people represents an as difficult as important challenge. In this context, the research work of the Department of Industrial Engineering of the University of Florence (UNIFI) led to a tailor-made wearable device for rehabilitative and assistive purposes. In this paper, the synthesis of the development process, sequentially ordered, is given.

Notes

Acknowledgements

The authors would like to thank the University of Florence and the Don Carlo Gnocchi foundation which have supported this work.

References

  1. 1.
    Heo, P., Gu, G., Lee, S.-J., Rhee, K., Kim, J.: Current hand exoskeleton technologies for rehabilitation and assistive engineering. Int. J. Precis. Eng. Manuf. 13(5), 807–824 (2012)CrossRefGoogle Scholar
  2. 2.
    Troncossi, M., Mozaffari-Foumashi, M., Parenti-Castelli, V.: An original classification of rehabilitation hand exoskeletons. J. Robot. Mech. Eng. Res. 1(4), 17–29 (2016)CrossRefGoogle Scholar
  3. 3.
    Conti, R., Meli, E., Ridolfi, A., Bianchi, M., Governi, L., Volpe, Y., Allotta, B.: Kinematic synthesis and testing of a new portable hand exoskeleton. Meccanica 52, 2873–2897 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Allotta, B., Conti, R., Governi, L., Meli, E., Ridolfi, A., Volpe, Y.: Development and experimental testing of a portable hand exoskeleton. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5339–5344, September 2015Google Scholar
  5. 5.
    Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Math. Program. 89(1), 149–185 (2000).  https://doi.org/10.1007/PL00011391MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Bianchi, M., Fanelli, F., Giordani, L., Ridolfi, A., Vannetti, F., Allotta, B.: An automatic scaling procedure for a wearable and portable handexoskeleton. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1–5, September 2016Google Scholar
  7. 7.
    Lobo-Prat, J., Kooren, P.N., Stienen, A.H., Herder, J.L., Koopman, B.F., Veltink, P.H.: Non-invasive control interfaces for intention detection in active movement-assistive devices. J. NeuroEngineering Rehabil. 11(1), 168 (2014).  https://doi.org/10.1186/1743-0003-11-168CrossRefGoogle Scholar
  8. 8.
    Adewuyi, A.A., Hargrove, L.J., Kuiken, T.A.: Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control. Front. Neurorobotics 10, 15 (2016).  https://doi.org/10.3389/fnbot.2016.00015CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicola Secciani
    • 1
    Email author
  • Matteo Bianchi
    • 1
  • Alessia Meschini
    • 1
  • Alessandro Ridolfi
    • 1
  • Yary Volpe
    • 1
  • Lapo Governi
    • 1
  • Benedetto Allotta
    • 1
  1. 1.Department of Industrial Engineering (DIEF)University of FlorenceFlorenceItaly

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