Simulation tools for measuring plantar mechanical comfort for the future footwear

The design and manufacture of footwear is evolving from a labour-intensive process to a process based on knowledge. This is clear from the large volume of new innovative technologies and manufacturing processes that have been developed in the last decade and cover a wide range of applications in the field of engineering, IT, materials, communication, etc. (CEC, 2009; Chituc et al., 2008; Wong et al, 2006; Pedrazzoli, 2009). Significant efforts were also made for the (mass) customization of final products in order to meet basic requirements of a population group or even individuals (Lee, 2006; Leng and Du, 2006; Luximon et al., 2003; Azariadis and Papagiannis, 2010).

During the last twenty years, a considerable number of works have been developed for the study of the biomechanical properties of footwear and their relationship with the kinematic of the foot or the so-called gait cycle. Modern studies use Computer Aided Engineering (CAE) technologies with three-dimensional foot models to reach conclusions about foot behavior and in some cases to improve some parameters related to shoe design. These tools are based on numerical methods and more specifically on the Finite Element Method (FEM). Through FEM it is possible to calculate realistic simulations concerning the behavior of the foot and of footwear. The most common biomechanical parameters calculated or simulated concern internal stresses, distribution of strains, deformations, etc.

In the modern practice of designing clothing and footwear products, comfort factors are also considered. The main reason is the relationship between comfort and performance. The entire product life-cycle is reconsidered in terms of design, performance and functionality, which is commonly perceived as footwear comfort. Given working conditions, the interaction of materials and product design with the human body corresponds to the physical and physiological side of comfort. In addition to these two aspects, there are psychological factors that shape the perception of comfort. Consumers increasingly demand personalized and differentiated shoes, which open opportunities for more creativity while ensuring that comfort and sustainability requests are satisfied.

Although the deployment of FEA for complex systems requires a high level of expertise, it is possible, once the analyses are parameterized and standardized, for non-experts to apply the techniques. This has been demonstrated in the Optshoes project ( for determining footwear comfort characteristics, where a simple to use web-based interface limits interactivity to parameterization of input files without any further involvement with the underlying FEA kernel (Zissis et al., 2016). The entire process is facilitated through using a specialized Materials Database.

Opt-shoes: main functions
Opt-shoes is a computer-aided engineering tool for supporting the design of footwear with desired comfort characteristics (Figure 1). It uses sole models with different industrial materials and a realistic foot bio-model. When standing and walking, ground forces act on the body through the sole of the shoe. Comfort can first be assessed in terms of pressure (force) distribution on the lower surface of the foot. High pressures indicate potential discomfort. Bending and torsional behavior of the footwear sole during walking are also critical. Different end uses of the footwear dictate various energy levels for sole bending and twisting. The system is able to optimize the sole stiffness by selecting the best available combination of the materials for the three sole layers which have a predefined – by the designer – desirable thickness. With this application, the designer can calculate stiffness levels and compare models to typical soles on the market ( ).


Figure 1: Interface of Opt-shoes (

The designer is able to choose from a list of industrial footwear materials, to select the stiffness of the sole, the thickness of each layer and the type of stress calculation for the sole layers. Figure 2 and Figure 3 depict an example of the stresses calculation for a flat three-layer sole of a casual footwear.




Figure 2: a) Input parameters for sole, b) Plantar pressures simulation results, c) Bending/Torsion simulation results


Figure 3: a) Input optimization parameters for sole, b) Output results for the three best matching sole layers combinations


  • Azariadis P. and Papagiannis P. (2010). A new business model for integrating textile/clothing and footwear production, The 3rd International Conference on Advanced Materials and Systems -ICAMS 2010, Bucharest, Sep. 16-18, 235-240.
  • Azariadis P., Moulianitis V., Alemany S., Olaso J., Jong D.P., Zande V.D.M. and Brands D., (2007), “Virtual Shoe Test Bed: A Computer-Aided Engineering Tool For Supporting Shoe Design”, CAD and Applications, 4(6), 741-750
  • CEC, 2009. CEC-made-shoe: Custom, Environment and Comfort made shoe. 2004-2008. Contact No 507378 – 2, FP6 IST – NMP (Manufacturing, Products and Service Engineering 2010),
  • Chituc C.M., Toscano C., Azevedo A., (2008). Interoperability in Collaborative Networks: Independent and industry-specific initiatives – The case of the footwear industry. Computers in Industry, 59(7):741-757.
  • Lee K., (2006). CAD System for Human-Centered Design. Computer-Aided Design & Applications,3(5):615-628.
  • Leng J. and Du R., (2006). A CAD Approach for Designing Customized Shoe Last. Computer-Aided Design & Applications, 3(1-4): 377-384.
  • Luximon A., Goonetilleke R.S., Tsui K.L., (2003). Footwear Fit Categorization. The Customer Centric Enterprise: Advances in Mass Customization and Personalization, Ed. Mitchell, M. Tseng, Frank Piller. Springer, pp. 491-500.
  • Pedrazzoli P., (2009). Design Of customeR dRiven shoes and multisiTe factory – DOROTHY. in: K.-D. Thoben, K. S. Pawar, & R. Goncalves (eds). 15th International Conference on Concurrent Enterprising. 22-24 June 2009, Leiden, Netherlands.
  • Wong K.H.M., Hui P.C.L., Chan A.C.K., (2006). Cryptography and authentication on RFID passive tags for apparel products. Computers in Industry, 57(4): 342-349
    Zissis D., Lekkas D., Azariadis Ph., Papanikos P., Xidias E., (2016). Collaborative CAD/CAE as a Cloud Service. International Journal of Systems Science: Operations & Logistics, 4:4, 339-355, doi:10.1080/23302674.2016.1186237

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