Classification method of tactile feeling using stacked autoencoder based on haptic primary colors

Fumihiro Kato, Charith Lasantha Fernando, Yasuyuki Inoue, Susumu Tachi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We have developed a classification method of tactile feeling using a stacked autoencoder-based neural network on haptic primary colors. The haptic primary colors principle is a concept of decomposing the human sensation of tactile feeling into force, vibration, and temperature. Images were obtained from variation in the frequency of the time series of the tactile feeling obtained when tracing a surface of an object, features were extracted by employing a stacked autoencoder using a neural network with two hidden layers, and supervised learning was conducted. We confirmed that the tactile feeling for three different surface materials can be classified with an accuracy of 82.0 [%].

本文言語English
ホスト出版物のタイトル2017 IEEE Virtual Reality, VR 2017 - Proceedings
出版社IEEE Computer Society
ページ391-392
ページ数2
ISBN(電子版)9781509066476
DOI
出版ステータスPublished - 2017 4月 4
外部発表はい
イベント19th IEEE Virtual Reality, VR 2017 - Los Angeles, United States
継続期間: 2017 3月 182017 3月 22

出版物シリーズ

名前Proceedings - IEEE Virtual Reality

Conference

Conference19th IEEE Virtual Reality, VR 2017
国/地域United States
CityLos Angeles
Period17/3/1817/3/22

ASJC Scopus subject areas

  • 工学(全般)

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