Vibrotactile Signal Generation by Deep Convolutional Generative Adversarial Network for Vibrotactile Displays

• AGATSUMA Shotaro

  University of Tsukuba

• KUROGI Jyunya

  Kumamoto University

• SAGA Satoshi

  Kumamoto University

• TAKAHASHI Shin

  University of Tsukuba

Bibliographic Information

Other Title

• 振動触覚ディスプレイのためのDCGANを用いた振動生成

Abstract

To create intuitive tactile displays, collecting vibrotactile information is important, though, the collection procedure requires manual scanning of textures. Thus, a collection of vast information is difficult. However, by employing machine learning technology, there is a possibility to generate further virtual data from existing collected data. In this paper, we made a generation model of vibrotactile signals from the collected acceleration data by using Deep Convolutional Generative Adversarial Network (DCGAN). We held two kinds of experiments to evaluate the performance of our DCGAN model and we consider the result of experiments.

Journal

• The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)

  The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (0), 1P2-U04-, 2019

  The Japan Society of Mechanical Engineers

Keywords

• machine learning
• vibrotactile signal generation
• acceleration

Details

• CRID: 1390846609787414016
• NII Article ID: 130007774559
• DOI: 10.1299/jsmermd.2019.1p2-u04
• ISSN: 24243124
• Web Site: https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1P2-U04/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed