Predict Afferent Tactile Neural Signal for Artificial Nerve Based on Finite Element Human Hand Model

  • Yuyang Wei
  • Guowu Wei
  • Lei RenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11740)


This paper aims to investigate the biomechanical aspect of human hand tactile perception by using finite element method and build the artificial neural nerve which can be interfaced with human afferent nerve. A subject-specific digital human hand finite element model (FE-DHHM) was developed based on CT and MR images. The geometries of phalanges, carpal bones, wrist bones, ligaments, tendons, subcutaneous tissue, epidermis and dermis were all included. The material properties were derived from in-vivo and in-vitro experiment results which are available in the literature, the boundary and loading conditions which were kinematic motion data and muscle forces, were captured based on the specific subject. This FE-DHHM was validated against in-vivo test results of the same subject based on contact pressure and contact areas. The whole active touch procedure was performed and simulated, the strain energy density near the locations of mechanoreceptors including slowly adapting type 1 (SA-I) and rapidly adapting (RA) were extracted and then used as inputs into the transduction and neural-dynamics (Izhikevivh neuro model) sub-model to predict neural spike or somatosensory information. A prototype of ‘artificial nerve’ which can produce the action potential signal is presented. Therefore the FE-DHHM presented in this paper can make a detailed and quantitative evaluation into biomechanical and neurophysiological aspects of human hand tactile perception and manipulation. The results obtained in this paper can be applied to design of bionic or neuro-robotic hand in the near future.


Tactile sensing Artificial afferent nerve Finite element human hand 


  1. 1.
    Alessandro, C., et al.: Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives. Front. Comput. Neurosci. 7, 43 (2013)CrossRefGoogle Scholar
  2. 2.
    Ajiboye, A.B., Weir, R.F.: Muscle synergies as a predictive framework for the EMG patterns of new hand postures. J. Neural Eng. 6(3), 036004 (2009)CrossRefGoogle Scholar
  3. 3.
    Israely, S., et al.: Muscle synergies control during hand-reaching tasks in multiple directions post-stroke. Front. Comput. Neurosci. 12, 10 (2018)CrossRefGoogle Scholar
  4. 4.
    Valle, G., et al.: Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis. Neuron 100(1), 37–45.e7 (2018)CrossRefGoogle Scholar
  5. 5.
    Kandel, E.R., et al.: Principles of Neural Science, vol. 4. McGraw-Hill, New York (2000)Google Scholar
  6. 6.
    Chamoret, D., Bodo, M., Roth, S.: A first step in finite-element simulation of a grasping task. Comput. Assist. Surg. 21(Suppl. 1), 22–29 (2016)CrossRefGoogle Scholar
  7. 7.
    Harih, G., Nohara, R., Tada, M.: Finite element digital human hand model-case study of grasping a cylindrical handle. J. Ergon. 07(02) (2017)Google Scholar
  8. 8.
    Harih, G., Dolsak, B.: Recommendations for tool-handle material choice based on finite element analysis. Appl. Ergon. 45(3), 577–585 (2014)CrossRefGoogle Scholar
  9. 9.
    Chamoret, D., et al.: A novel approach to modelling and simulating the contact behaviour between a human hand model and a deformable object. Comput. Methods Biomech. Biomed. Eng. 16(2), 130–140 (2013)CrossRefGoogle Scholar
  10. 10.
    Pham, T.Q., et al.: An FE simulation study on population response of RA-I mechanoreceptor to different widths of square indenter. SICE J. Control Meas. Syst. Integr. 10(5), 426–432 (2017)CrossRefGoogle Scholar
  11. 11.
    Yao, M., Wang, R.: Neurodynamic analysis of Merkel cell–neurite complex transduction mechanism during tactile sensing. Cogn. Neurodyn. 13, 293–302 (2018)CrossRefGoogle Scholar
  12. 12.
    Gerling, G.J., Thomas, G.W.: Fingerprint lines may not directly affect SA-I mechanoreceptor response. Somatosens. Mot. Res. 25(1), 61–76 (2008)CrossRefGoogle Scholar
  13. 13.
    Gerling, G.J., et al.: Validating a population model of tactile mechanotransduction of slowly adapting type I afferents at levels of skin mechanics, single-unit response and psychophysics. IEEE Trans. Haptics 7(2), 216–228 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dandekar, K., Raju, B.I., Srinivasan, M.A.: 3-D finite-element models of human and monkey fingertips to investigate the mechanics of tactile sense. J. Biomech. Eng. 125(5), 682–691 (2003)CrossRefGoogle Scholar
  15. 15.
    Pham, T.Q., et al.: Effect of 3D microstructure of dermal papillae on SED concentration at a mechanoreceptor location. PLoS ONE 12(12), e0189293 (2017)CrossRefGoogle Scholar
  16. 16.
    Gong, H., et al.: Preliminary study on SED distribution of tactile sensation in fingertip. MATEC Web Conf. 45, 04006 (2016)CrossRefGoogle Scholar
  17. 17.
    Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks 15(5), 1063–1070 (2004)CrossRefGoogle Scholar
  18. 18.
    Zhengkun, Y., Yilei, Z.: Recognizing tactile surface roughness with a biomimetic fingertip: a soft neuromorphic approach. Neurocomputing 244, 102–111 (2017)CrossRefGoogle Scholar
  19. 19.
    Oddo, C.M., et al.: Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons. Sci. Rep. 8, 45898 (2017)CrossRefGoogle Scholar
  20. 20.
    Salimi-Nezhad, N., et al.: A digital hardware realization for spiking model of cutaneous mechanoreceptor. Front. Neurosci. (2018)Google Scholar
  21. 21.
    Phillips, J.R., Johnson, K.O.: Tactile spatial resolution. II. Neural representation of bars, edges, and gratings in monkey primary afferents. J. Neurophysiol. 46(6), 1192–1203 (1981)CrossRefGoogle Scholar
  22. 22.
    Knibestöl, M., Vallbo, Å.B.: Single unit analysis of mechanoreceptor activity from the human glabrous skin. Acta Physiol. Scand. 80(2), 178–195 (1970)CrossRefGoogle Scholar
  23. 23.
    Yi, Z., Zhang, Y., Peters, J.: Biomimetic tactile sensors and signal processing with spike trains: a review. Sens. Actuators A: Phys. 269, 41–52 (2018)CrossRefGoogle Scholar
  24. 24.
    Bologna, L., et al.: A closed-loop neurobotic system for fine touch sensing. J. Neural Eng. 10(4), 046019 (2013)CrossRefGoogle Scholar
  25. 25.
    Oddo, C.M., et al.: Roughness encoding for discrimination of surfaces in artificial active-touch. IEEE Trans. Rob. 27(3), 522–533 (2011)CrossRefGoogle Scholar
  26. 26.
    Yi, Z., Zhang, Y., Peters, J.: Bioinspired tactile sensor for surface roughness discrimination. Sens. Actuators A: Phys. 255, 46–53 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mechanical, Aerospace and Civil EngineeringThe University of ManchesterManchesterUK
  2. 2.School of Computing, Science and EngineeringUniversity of SalfordSalfordUK

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