Effective awaking interaction learning system that uses 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 publication2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings
Pages104-108
Number of pages5
DOIs
Publication statusPublished - 2013 Apr 26
Externally publishedYes
Event8th IEEE Sensors Applications Symposium, SAS 2013 - Galveston, TX, United States
Duration: 2013 Feb 192013 Feb 21

Publication series

Name2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings

Other

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

Keywords

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

ASJC Scopus subject areas

  • Hardware and Architecture

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  • Cite this

    Nakase, J., Moriyama, K., Kiyokawa, K., Numao, M., Oyama, M., & Kurihara, S. (2013). Effective awaking interaction learning system that uses vital sensing. In 2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings (pp. 104-108). [6493566] (2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings). https://doi.org/10.1109/SAS.2013.6493566