TY - GEN
T1 - Morphological Computation of Skin Focusing on Fingerprint Structure
AU - Musha, Akane
AU - Daihara, Manabu
AU - Shigemune, Hiroki
AU - Sawada, Hideyuki
N1 - Funding Information:
Acknowledgment. The authors express gratitude towards Dr. Kohei Nakajima for the discussion about the reservoir computing. We also thank Dr. Helmut Hauser for sharing the implementation codes about the morphological computation. This work was supported by JSPS KAKENHI Grant Nos. 18H05473 and 18H05895.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Fingerprints
KW - Morphological computation
KW - Reservoir computing
KW - Skin
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U2 - 10.1007/978-3-030-61616-8_38
DO - 10.1007/978-3-030-61616-8_38
M3 - Conference contribution
AN - SCOPUS:85094178096
SN - 9783030616151
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 470
EP - 481
BT - Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
A2 - Farkaš, Igor
A2 - Masulli, Paolo
A2 - Wermter, Stefan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Artificial Neural Networks, ICANN 2020
Y2 - 15 September 2020 through 18 September 2020
ER -