Learning system for adapting users with user's state classification by vital sensing

Junya Nakase, Koichi Moriyama, Kiyoshi Kiyokawa, Masayuki Numao, Mayumi Oyama, Satoshi Kurihara

研究成果: Conference contribution

抄録

In ambient information systems, not only extracting human behavior with a sensor network but also adaptive autonomous interaction between the environment and humans is an important function. In this paper, we propose a reinforcement learning methodology for acquiring suitable interaction for each person's daily behavior. This time, we used vital sensors to detect and classify a user's condition. In an experiment, we show the feasibility of the proposed methodology.

本文言語English
ホスト出版物のタイトルIEEE Virtual Reality Conference 2013, VR 2013 - Proceedings
DOI
出版ステータスPublished - 2013
外部発表はい
イベント20th IEEE Virtual Reality Conference, VR 2013 - Orlando, FL, United States
継続期間: 2013 3 162013 3 20

出版物シリーズ

名前Proceedings - IEEE Virtual Reality

Other

Other20th IEEE Virtual Reality Conference, VR 2013
CountryUnited States
CityOrlando, FL
Period13/3/1613/3/20

ASJC Scopus subject areas

  • Engineering(all)

フィンガープリント 「Learning system for adapting users with user's state classification by vital sensing」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル