Home // ACHI 2020, The Thirteenth International Conference on Advances in Computer-Human Interactions // View article
Authors:
Shotaro Agatsuma
Junya Kurogi
Satoshi Saga
Simona Vasilache
Shin Takahashi
Keywords: Acceleration, Generative Adversarial Networks, Vibrotactile Display.
Abstract:
Various kinds of vibrotactile information have been recorded from real textures and used to present high-quality tactile sensations via tactile displays. However, it is unrealistic to collect large amounts of vibrotactile data under many different conditions. Thus, we develop a method whereby recorded data can be changed to represent conditions differing from those at the time of initial recording. In the first step, we construct a data generation model using a Generative Adversarial Network (GAN). The model makes simple calculations and generates unknown data from recorded acceleration data obtained by rubbing real objects. The model can generate three-axis, time-series data. To evaluate the quality of the data generated, we devised a string-based tactile display and presented generated vibrotactile information to users. Users reported that the generated data were indistinguishable from real data.
Pages: 19 to 24
Copyright: Copyright (c) IARIA, 2020
Publication date: March 22, 2020
Published in: conference
ISSN: 2308-4138
ISBN: 978-1-61208-761-0
Location: Valencia, Spain
Dates: from November 21, 2020 to November 25, 2020