Biomechanical mechanism of fabric softness discrimination


“Softness” is one of the primitive terms describing the physical and sensory attributes of fabric, however, the information for its physiological mechanism compared to statistical physical factors of fabric softness is scarce. To explain the biomechanical and the potential neurophysiological phenomenon for exploring fabric softness, a finger-fabric finite element model is used to conduct an active contact simulation analysis. The effects of surface friction index and compression modulus of fabric on softness discrimination are investigated. The interests of the study are in the contributions of these fabric variables to the changing contact area, interfacial friction shear stress and contact pressure distributions, which are significant cognitive variables or stimulus parameters in peripheral neural levels documented by prior observations. The mechanistic data for fingerpad-fabric interaction indicate that the basis for the perception of softness of flexible and bulk fabric is likely on the spatial variation of the pressure and shear stress on the skin resulting from the surface friction index and compression property of fabric. These conclusions suggest that devices for the haptic rendering of fabric softness, based on vibration result from surface roughness, is not sufficient to perceive the soft-touch feel of fabric as a result of the cues with lack information for the changing contact area by touch.

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  1. 1.

    S. Kawabata and M. Niwa,J. Text. Inst.,80, 19 (1989).

    Google Scholar 

  2. 2.

    D. Picard, C. Dacremont, D. Valentin, and A. Giboreau,Acta Psychologica,114, 165 (2003).

    Article  Google Scholar 

  3. 3.

    H. Elder, S. Fisher, and K. Armatrong,J. Text. Inst.,75, 37 (1984).

    Google Scholar 

  4. 4.

    S. L. Ali and S. Begum,Ergonomics,37, 801 (1994).

    Article  CAS  Google Scholar 

  5. 5.

    Y. J. Jeong and T. J. Kang,J. Text. Inst.,92, 1 (2001).

    Google Scholar 

  6. 6.

    F. T. Peirce,J. Text. Inst.,21, 377 (1930).

    Google Scholar 

  7. 7.

    S. Kaiser, N. Pan, and J. Chandler, NTC Project: I02-E04, 2003; S02-CD04, 2004.

  8. 8.

    Y. Chen, B. Collier, and P. Hu,Text. Res. J.,70, 443 (2000).

    Article  CAS  Google Scholar 

  9. 9.

    S. Perkamian,Text. Res. J.,70, 201 (2000).

    Article  Google Scholar 

  10. 10.

    J. O. Ajayi and H. M. Elder,J. Text. Inst.,88, 232 (1997).

    Google Scholar 

  11. 11.

    M. A. Srinivasan and R. H. Lamotte,J. Neurophysiology,73, 88 (1995).

    CAS  Google Scholar 

  12. 12.

    H. Z. Tan, N. L. Durlach, and M. A. Srinivasan,Perception & Psychophysics,57, 495 (1995).

    CAS  Google Scholar 

  13. 13.

    R. H. LaMotte,J. Neurophysiology,83, 1777 (2000).

    CAS  Google Scholar 

  14. 14.

    G. Ambroci, A. Bicchi, and D. D. Rocci,IEEE Transactions on Robotics and Automation,16, 496 (2000).

    Article  Google Scholar 

  15. 15.

    J. Z. Wu and R. G. Dong,Proc. IMechE, Part H: J. Engineering in Medicine,219, 89 (2005).

    Article  CAS  Google Scholar 

  16. 16.

    S. de Jong and R. Postle,J. Text. Inst.,68, 350 (1977).

    Google Scholar 

  17. 17.

    B. K. Behera and S. B. Muttagi,J. Text. Eng.,51, 1 (2005).

    Article  Google Scholar 

  18. 18.

    S. S. Ramkumar, D. J. Wood, K. Fox, and S. C. Harlock,Text. Res. J.,73, 469 (2003).

    Article  CAS  Google Scholar 

  19. 19.

    S. Johansson and G. Westling,Exp. Brain Res.,66, 141 (1988).

    Google Scholar 

  20. 20.

    J. Mei, R. P. Tuckett, K. W. Horch, C. M. Ballinger, and D. A. Poulos,J. Neuroscience,3, 1572 (1983).

    Google Scholar 

  21. 21.

    G. Huang, D. Metaxas, and M. Govindarai in “Eurographics/SIGGRAPH Symposium on Computer Animation” (D. Breen and M. Lin Eds.), p.52, 2003.

  22. 22.

    J. Y. Hu, X. Ding, and R. B. Wang, “Proceedings of the 2nd International Conference on Dynamics, Vibration and Control (CD Version)”, p.95, Beijing, China, 2006.

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Correspondence to Xin Ding.

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Hu, J., Ding, X. & Wang, R. Biomechanical mechanism of fabric softness discrimination. Fibers Polym 8, 372–376 (2007).

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  • Fabric softness
  • Finite element method
  • Surface friction index
  • Compressibility