Effective awaking interaction learning system that uses 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2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings
ページ104-108
ページ数5
DOI
出版ステータスPublished - 2013
外部発表はい
イベント8th IEEE Sensors Applications Symposium, SAS 2013 - Galveston, TX, United States
継続期間: 2013 2 192013 2 21

出版物シリーズ

名前2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings

Other

Other8th IEEE Sensors Applications Symposium, SAS 2013
国/地域United States
CityGalveston, TX
Period13/2/1913/2/21

ASJC Scopus subject areas

  • ハードウェアとアーキテクチャ

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