TY - GEN
T1 - Benchmarking learning networks on eat-sleep conditions
AU - Parque, Victor
AU - Obasekore, Hammed
AU - Oladayo, Solomon
AU - Miyashita, Tomoyuki
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Human activity recognition technologies are key to promote healthy life styles, and potential to offer explanations to study the origin of complex diseases. In particular, it is well-known that the quick transition between eating and sleeping is known to trigger unfavorable conditions for healthy life style. In this paper we describe our observations and insights in the benchmarking of the state of the art classification models based on graph representations to classify activities comprising drinking, eating, walking, running and sleeping.
AB - Human activity recognition technologies are key to promote healthy life styles, and potential to offer explanations to study the origin of complex diseases. In particular, it is well-known that the quick transition between eating and sleeping is known to trigger unfavorable conditions for healthy life style. In this paper we describe our observations and insights in the benchmarking of the state of the art classification models based on graph representations to classify activities comprising drinking, eating, walking, running and sleeping.
UR - http://www.scopus.com/inward/record.url?scp=85074876070&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074876070&partnerID=8YFLogxK
U2 - 10.1109/LifeTech.2019.8883998
DO - 10.1109/LifeTech.2019.8883998
M3 - Conference contribution
AN - SCOPUS:85074876070
T3 - 2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019
SP - 29
EP - 30
BT - 2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019
Y2 - 12 March 2019 through 14 March 2019
ER -
