TY - GEN
T1 - Learning system for adapting users with user's state classification by vital sensing
AU - Nakase, Junya
AU - Moriyama, Koichi
AU - Kiyokawa, Kiyoshi
AU - Numao, Masayuki
AU - Oyama, Mayumi
AU - Kurihara, Satoshi
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - ambient information system
KW - interaction sequence
KW - profit sharing
KW - reinforcement learning
KW - vital sensing
UR - http://www.scopus.com/inward/record.url?scp=84884849263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884849263&partnerID=8YFLogxK
U2 - 10.1109/VR.2013.6549438
DO - 10.1109/VR.2013.6549438
M3 - Conference contribution
AN - SCOPUS:84884849263
SN - 9781467347952
T3 - Proceedings - IEEE Virtual Reality
BT - IEEE Virtual Reality Conference 2013, VR 2013 - Proceedings
T2 - 20th IEEE Virtual Reality Conference, VR 2013
Y2 - 16 March 2013 through 20 March 2013
ER -