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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIEEE Virtual Reality Conference 2013, VR 2013 - Proceedings
DOIs
Publication statusPublished - 2013
Event20th IEEE Virtual Reality Conference, VR 2013 - Orlando, FL, United States
Duration: 2013 Mar 162013 Mar 20

Publication series

NameProceedings - IEEE Virtual Reality

Other

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

Keywords

  • ambient information system
  • interaction sequence
  • profit sharing
  • reinforcement learning
  • vital sensing

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Nakase, J., Moriyama, K., Kiyokawa, K., Numao, M., Oyama, M., & Kurihara, S. (2013). Learning system for adapting users with user's state classification by vital sensing. In IEEE Virtual Reality Conference 2013, VR 2013 - Proceedings [6549438] (Proceedings - IEEE Virtual Reality). https://doi.org/10.1109/VR.2013.6549438