Name
Abstract | ||
|---|---|---|
Various methods have been proposed for collecting vibrotactile information. However, the collection procedure requires manual scanning of texture, collection of vast information may be difficult. Owing to the fast progress of machine learning technologies, even with little information, there is a possibility to generate further virtual data from existing collected data by using Generative Advisory Network (GAN). In this paper, we proposed a generation model of vibrotactile information by Deep Convolutional GAN (DCGAN) from the collected acceleration data. We generated various vibrotactile information by using the proposed DCGAN, and compared the tactile stimulation based on the generated data with the actual texture. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-981-13-3194-7_12 | Lecture Notes in Electrical Engineering |
Keywords | DocType | Volume |
Vibrotactile information,Acceleration,DCGAN | Conference | 535 |
ISSN | Citations | PageRank |
1876-1100 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shotaro Agatsuma | 1 | 0 | 0.68 |
| Junya Kurogi | 2 | 0 | 0.34 |
| Satoshi Saga | 3 | 68 | 11.90 |
| Simona Vasilache | 4 | 1 | 2.99 |
| Shin Takahashi | 5 | 191 | 29.40 |