TY - GEN
T1 - Effective awaking interaction learning system that uses 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=84876483832&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876483832&partnerID=8YFLogxK
U2 - 10.1109/SAS.2013.6493566
DO - 10.1109/SAS.2013.6493566
M3 - Conference contribution
AN - SCOPUS:84876483832
SN - 9781467346351
T3 - 2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings
SP - 104
EP - 108
BT - 2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings
T2 - 8th IEEE Sensors Applications Symposium, SAS 2013
Y2 - 19 February 2013 through 21 February 2013
ER -