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Automatic Below-Knee Prosthesis Socket Design: A Preliminary Approach

  • Giorgio Colombo
  • Giancarlo FacoettiEmail author
  • Caterina Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9745)

Abstract

In this work we present a preliminary study on a system able to design automatically sockets for lower-limb prosthesis. The socket is the most important part of the whole prosthesis and requires a custom design specific for the patient’s characteristics and her/his residuum morphology. The system takes in input the weight and the lifestyle of the patient, the tonicity level and the geometry file of the residuum, and creates a new model applying the correct geometric deformations needed to create a functional socket. In fact, in order to provide the right fit and prevent pain, we need to create on the socket load and off-load zones in correspondence of the critical anatomical areas. To identify the position of such critical areas, several neural networks have been trained using a dataset generated from real residuum models.

Keywords

Lower limb prosthesis Neural network Prosthetic socket CAD 

References

  1. 1.
    Buzzi, M., Colombo, G., Facoetti, G., Gabbiadini, S., Rizzi, C.: 3D modelling and knowledge: tools to automate prosthesis development process. Int. J. Interact. Des. Manuf. 6(1), 41–53 (2012)CrossRefGoogle Scholar
  2. 2.
    Colombo, G., Facoetti, G., Rizzi, C., Vitali, A.: Automatic identification of below-knee residuum anatomical zones. In: Duffy, V.G. (ed.) DHM 2015. LNCS, vol. 9185, pp. 327–335. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  3. 3.
    Lee, W.C., Zhang, M., Mak, A.F.: Regional differences in pain threshold and tolerance of the transtibial residual limb: including the effects of age and interface material. Arch. Phys. Med. Rehabil. 86, 641–649 (2005)CrossRefGoogle Scholar
  4. 4.
    Haykin, S.: Neural Networks and Learning Machines – Pearson. Prentice Hall, USA (2008)Google Scholar
  5. 5.
    Bishop, C.M.: Pattern Recognition and Machine Learning - Information Science and Statistics. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  6. 6.
    Jadeberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. In: Workshop on Deep Learning and Representation Learning (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giorgio Colombo
    • 1
  • Giancarlo Facoetti
    • 2
    Email author
  • Caterina Rizzi
    • 3
  1. 1.Department of Mechanical EngineeringPolytechnic of MilanMilanItaly
  2. 2.BigFlo s.r.l. (BG)DalmineItaly
  3. 3.Department of Management, Information and Production EngineeringUniversity of Bergamo (BG)DalmineItaly

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