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

Momoko Sagara, Kenjiro Takemura

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665484640
DOI
出版ステータスPublished - 2022
イベント2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States
継続期間: 2022 10月 302022 11月 2

出版物シリーズ

名前Proceedings of IEEE Sensors
2022-October
ISSN(印刷版)1930-0395
ISSN(電子版)2168-9229

Conference

Conference2022 IEEE Sensors Conference, SENSORS 2022
国/地域United States
CityDallas
Period22/10/3022/11/2

ASJC Scopus subject areas

  • 電子工学および電気工学

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