TY - GEN
T1 - A development of classification model for smartphone addiction recognition system based on smartphone usage data
AU - Lawanont, Worawat
AU - Inoue, Masahiro
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant number 15K00929.
Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - The rapid growth of smartphone in recent years has resulted in many syndromes. Most of these syndromes are caused by excessive use of smartphone. In addition, people who tends to use smartphone excessively are also likely to have smartphone addiction. In this paper, we presented the system architecture for e-Health system. Not only we used the architecture for our smartphone addiction recognition system, but we also pointed out important benefits of the system architecture, which also can be adopted by other system. Later on, we presented a development of the classification model for recognizing likelihood of having smartphone addiction. We trained the classification model based on data retrieved from subjects’ smartphone. The result showed that the best model can correctly classify the instance up to 78%.
AB - The rapid growth of smartphone in recent years has resulted in many syndromes. Most of these syndromes are caused by excessive use of smartphone. In addition, people who tends to use smartphone excessively are also likely to have smartphone addiction. In this paper, we presented the system architecture for e-Health system. Not only we used the architecture for our smartphone addiction recognition system, but we also pointed out important benefits of the system architecture, which also can be adopted by other system. Later on, we presented a development of the classification model for recognizing likelihood of having smartphone addiction. We trained the classification model based on data retrieved from subjects’ smartphone. The result showed that the best model can correctly classify the instance up to 78%.
KW - Activity recognition
KW - Data mining
KW - Smartphone addiction
KW - Smartphone application
KW - e-Health system
UR - http://www.scopus.com/inward/record.url?scp=85020456424&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020456424&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59424-8_1
DO - 10.1007/978-3-319-59424-8_1
M3 - Conference contribution
AN - SCOPUS:85020456424
SN - 9783319594231
T3 - Smart Innovation, Systems and Technologies
SP - 3
EP - 12
BT - Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
A2 - Jain, Lakhmi C.
A2 - Czarnowski, Ireneusz
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
Y2 - 21 June 2017 through 23 June 2017
ER -