A development of classification model for smartphone addiction recognition system based on smartphone usage data

Worawat Lawanont, Masahiro Inoue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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%.

LanguageEnglish
Title of host publicationIntelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-12
Number of pages10
Volume73
ISBN (Print)9783319594231
DOIs
StatePublished - 2018
Externally publishedYes
Event9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017 - Vilamoura, Portugal
Duration: 2017 Jun 212017 Jun 23

Publication series

NameSmart Innovation, Systems and Technologies
Volume73
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Other

Other9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
CountryPortugal
CityVilamoura
Period17/6/2117/6/23

Fingerprint

Smartphones
Addiction
Health
System architecture

Keywords

  • Activity recognition
  • Data mining
  • e-Health system
  • Smartphone addiction
  • Smartphone application

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

Cite this

Lawanont, W., & Inoue, M. (2018). A development of classification model for smartphone addiction recognition system based on smartphone usage data. In Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017 (Vol. 73, pp. 3-12). (Smart Innovation, Systems and Technologies; Vol. 73). Springer Science and Business Media Deutschland GmbH. DOI: 10.1007/978-3-319-59424-8_1

A development of classification model for smartphone addiction recognition system based on smartphone usage data. / Lawanont, Worawat; Inoue, Masahiro.

Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017. Vol. 73 Springer Science and Business Media Deutschland GmbH, 2018. p. 3-12 (Smart Innovation, Systems and Technologies; Vol. 73).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lawanont, W & Inoue, M 2018, A development of classification model for smartphone addiction recognition system based on smartphone usage data. in Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017. vol. 73, Smart Innovation, Systems and Technologies, vol. 73, Springer Science and Business Media Deutschland GmbH, pp. 3-12, 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017, Vilamoura, Portugal, 17/6/21. DOI: 10.1007/978-3-319-59424-8_1
Lawanont W, Inoue M. A development of classification model for smartphone addiction recognition system based on smartphone usage data. In Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017. Vol. 73. Springer Science and Business Media Deutschland GmbH. 2018. p. 3-12. (Smart Innovation, Systems and Technologies). Available from, DOI: 10.1007/978-3-319-59424-8_1
Lawanont, Worawat ; Inoue, Masahiro. / A development of classification model for smartphone addiction recognition system based on smartphone usage data. Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017. Vol. 73 Springer Science and Business Media Deutschland GmbH, 2018. pp. 3-12 (Smart Innovation, Systems and Technologies).
@inproceedings{a5eefb8cc4694082ac2a29962a7f0738,
title = "A development of classification model for smartphone addiction recognition system based on smartphone usage data",
abstract = "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{\%}.",
keywords = "Activity recognition, Data mining, e-Health system, Smartphone addiction, Smartphone application",
author = "Worawat Lawanont and Masahiro Inoue",
year = "2018",
doi = "10.1007/978-3-319-59424-8_1",
language = "English",
isbn = "9783319594231",
volume = "73",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--12",
booktitle = "Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017",

}

TY - GEN

T1 - A development of classification model for smartphone addiction recognition system based on smartphone usage data

AU - Lawanont,Worawat

AU - Inoue,Masahiro

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 - e-Health system

KW - Smartphone addiction

KW - Smartphone application

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

SN - 9783319594231

VL - 73

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

PB - Springer Science and Business Media Deutschland GmbH

ER -