1. Proceedings
  2. I3M
  3. 2023
  4. IWISH
  5. 10.46354/i3m.2023.iwish.005

Towards an Automated Process for Adaptive Modelling of Orthoses and Shoe Insoles in Additive Manufacturing 

  • a Gerald A. Zwettler , 
  • b Martin Trixner, 
  • c Clemens Schartmüller, 
  • d Sophie Bauernfeind, 
  • e Thomas Stockinger, 
  • f Christoph Praschl 
  • a,f,d  Research Group Advanced Information Systems and Technology, Research and Development Department, University of Applied Sciences Upper Austria 
  • a Department of Software Engineering, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria 
  • bWAKO 3D GmbH (Ltd.), Granitweg 1, 4202 Kirchschlag bei Linz, Austria 
  • acsendance GmbH (Ltd.), Pulvermühlstrasse 3, 4040 Linz, Austria 
Cite as
Zwettler, G.A., Trixner, M., Schartmüller, C., Bauernfeind, S., Stockinger, T., Praschl, C. (2023). Towards an Automated Process for Adaptive Modelling of Orthoses and Shoe Insoles in Additive Manufacturing. Proceedings of the 12th International Workshop on Innovative Simulation for Healthcare (IWISH 2023).,005. DOI: https://doi.org/10.46354/i3m.2023.iwish.005

Abstract

Although orthopedics is becoming increasingly important as a medical domain, especially in emerging countries, the level of automation is still marginal and hardly any Industry 4.0 paradigms have been implemented. In this scientific work, solution concepts for holistic process automation in orthopedics are introduced so that prosthetic covers and orthoses for different body regions can be automated by using AI and evaluated with sensor networks. In this process, body scan models are adapted to the conditions of the anatomy or prosthesis models, so that stability as well as fitting accuracy are given in comparison with the other half of the body. Automation in the field of orthopedics leads not only to a significant reduction in costs but can also help to close the research gap regarding objectifiability of results. The first partial aspects have already been successfully implemented for leg prostheses, arm prostheses and shoe insoles with the aid of machine learning processes and physical models for elastic form fitting. As soon as the overall process has been realized, the applicability will be validated in the following year of the project by means of clinical studies and evaluated by utilizing sensor networks for pressure and temperature measurements.

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