Morphological Computation of Skin Focusing on Fingerprint Structure

Akane Musha*, Manabu Daihara, Hiroki Shigemune, Hideyuki Sawada

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


When humans get tactile sensation, we touch an object with the skin and the stimuli are transmitted to the brain. The effect of the skin in tactile perception however has not been clarified yet, and sensors considering the skin functions are not introduced. In this research, we investigate the information processing performed by the skin against physical stimuli in touching an object from the viewpoint of morphological computation. We create a dynamical model that expresses the skin structure based on the spring and mass model, and show that the model contributes to the learning of temporal response against physical stimuli. In addition, we conduct an experiment to compare the learning performance of a finger model having fingerprints with a model without fingerprints. Frequency response against physical stimuli with different frequencies is examined, and the result shows that the performance of a model with fingerprints is better in the higher frequency range. The model with fingerprints also reflects the hardness of the human skin remarkably. These results are expected to help clarify the information processing ability of the human skin focusing on the fingerprint structure in response to external physical stimuli.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030616151
Publication statusPublished - 2020
Event29th International Conference on Artificial Neural Networks, ICANN 2020 - Bratislava, Slovakia
Duration: 2020 Sept 152020 Sept 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12397 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Artificial Neural Networks, ICANN 2020


  • Fingerprints
  • Morphological computation
  • Reservoir computing
  • Skin

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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