Texture classification model based on temporal changes in vibration using wavelet transform

Momoko Sagara, Kenjiro Takemura

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


Tactile sensation is important in the perception of the external world, and research on texture classification using vibration data obtained by tracing an object has been widely conducted. However, few studies have utilized time-varying frequency components, which are thought to be recognized by moving their fingers back and forth when they feel tactile sensations. Therefore, we propose a new texture classification system that uses the time variation of vibration with which a latent vector effective in perceiving tactile sensations is possibly embedded. Vibration data was acquired by reciprocating the developed sensor with strain gauges and PVDF film on fifteen different samples. The wavelet transform of the vibration data was conducted to extract a scalogram containing time-varying information. A CNN was constructed to perform texture classification based on the scalograms, resulting in an accurate classification. The results also showed the robustness of the model regarding the vibration information against the different touch condition.

Original languageEnglish
Title of host publication2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665484640
Publication statusPublished - 2022
Event2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States
Duration: 2022 Oct 302022 Nov 2

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference2022 IEEE Sensors Conference, SENSORS 2022
Country/TerritoryUnited States


  • CNN
  • tactile motion
  • tactile sensor
  • texture classification
  • wavelet transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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