Fuzzy modeling to ‘understand’ personal preferences of mHealth users: a case study

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

21 Downloads (Pure)

Abstract

This case study evaluates to what extent personal preferences can be automatically derived from user event data in an mHealth setting. Based on a theoretical framework, user preferences are described using six classes. Based on this framework, a structure of six Takagi-Sugeno fuzzy inference systems was constructed and evaluated against baseline data from an official survey for measuring the framework's constructs. From this analysis, it was found that user preferences may be derived from user event data using fuzzy modeling with accuracy scores that are higher than a random predictor would typically achieve.
Original languageEnglish
Title of host publication2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PublisherAtlantis Press
Pages558-565
Number of pages8
Volume1
ISBN (Electronic)978-94-6252-770-6
DOIs
Publication statusPublished - Sep 2019
Event11th Conference of the European Society for Fuzzy Logic and Technology - Prague, Czech Republic
Duration: 9 Sep 201913 Sep 2019

Conference

Conference11th Conference of the European Society for Fuzzy Logic and Technology
CountryCzech Republic
CityPrague
Period9/09/1913/09/19

Fingerprint

Fuzzy inference
mHealth

Keywords

  • fuzzy inference system
  • Takagi-Sugeno
  • personalization
  • mHealth

Cite this

Nuijten, R., Kaymak, U., Van Gorp, P., Simons, M., van den Berg, P., & Le Blanc, P. (2019). Fuzzy modeling to ‘understand’ personal preferences of mHealth users: a case study. In 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (Vol. 1, pp. 558-565). Atlantis Press. https://doi.org/10.2991/eusflat-19.2019.77
Nuijten, Raoul ; Kaymak, Uzay ; Van Gorp, Pieter ; Simons, Monique ; van den Berg, Pauline ; Le Blanc, Pascale. / Fuzzy modeling to ‘understand’ personal preferences of mHealth users : a case study. 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Vol. 1 Atlantis Press, 2019. pp. 558-565
@inproceedings{75bbd7a3a8004a59b96442fa444e1ca3,
title = "Fuzzy modeling to ‘understand’ personal preferences of mHealth users: a case study",
abstract = "This case study evaluates to what extent personal preferences can be automatically derived from user event data in an mHealth setting. Based on a theoretical framework, user preferences are described using six classes. Based on this framework, a structure of six Takagi-Sugeno fuzzy inference systems was constructed and evaluated against baseline data from an official survey for measuring the framework's constructs. From this analysis, it was found that user preferences may be derived from user event data using fuzzy modeling with accuracy scores that are higher than a random predictor would typically achieve.",
keywords = "fuzzy inference system, Takagi-Sugeno, personalization, mHealth",
author = "Raoul Nuijten and Uzay Kaymak and {Van Gorp}, Pieter and Monique Simons and {van den Berg}, Pauline and {Le Blanc}, Pascale",
year = "2019",
month = "9",
doi = "10.2991/eusflat-19.2019.77",
language = "English",
volume = "1",
pages = "558--565",
booktitle = "2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)",
publisher = "Atlantis Press",
address = "Netherlands",

}

Nuijten, R, Kaymak, U, Van Gorp, P, Simons, M, van den Berg, P & Le Blanc, P 2019, Fuzzy modeling to ‘understand’ personal preferences of mHealth users: a case study. in 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). vol. 1, Atlantis Press, pp. 558-565, 11th Conference of the European Society for Fuzzy Logic and Technology, Prague, Czech Republic, 9/09/19. https://doi.org/10.2991/eusflat-19.2019.77

Fuzzy modeling to ‘understand’ personal preferences of mHealth users : a case study. / Nuijten, Raoul; Kaymak, Uzay; Van Gorp, Pieter; Simons, Monique; van den Berg, Pauline; Le Blanc, Pascale.

2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Vol. 1 Atlantis Press, 2019. p. 558-565.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Fuzzy modeling to ‘understand’ personal preferences of mHealth users

T2 - a case study

AU - Nuijten, Raoul

AU - Kaymak, Uzay

AU - Van Gorp, Pieter

AU - Simons, Monique

AU - van den Berg, Pauline

AU - Le Blanc, Pascale

PY - 2019/9

Y1 - 2019/9

N2 - This case study evaluates to what extent personal preferences can be automatically derived from user event data in an mHealth setting. Based on a theoretical framework, user preferences are described using six classes. Based on this framework, a structure of six Takagi-Sugeno fuzzy inference systems was constructed and evaluated against baseline data from an official survey for measuring the framework's constructs. From this analysis, it was found that user preferences may be derived from user event data using fuzzy modeling with accuracy scores that are higher than a random predictor would typically achieve.

AB - This case study evaluates to what extent personal preferences can be automatically derived from user event data in an mHealth setting. Based on a theoretical framework, user preferences are described using six classes. Based on this framework, a structure of six Takagi-Sugeno fuzzy inference systems was constructed and evaluated against baseline data from an official survey for measuring the framework's constructs. From this analysis, it was found that user preferences may be derived from user event data using fuzzy modeling with accuracy scores that are higher than a random predictor would typically achieve.

KW - fuzzy inference system

KW - Takagi-Sugeno

KW - personalization

KW - mHealth

U2 - 10.2991/eusflat-19.2019.77

DO - 10.2991/eusflat-19.2019.77

M3 - Conference contribution

VL - 1

SP - 558

EP - 565

BT - 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)

PB - Atlantis Press

ER -

Nuijten R, Kaymak U, Van Gorp P, Simons M, van den Berg P, Le Blanc P. Fuzzy modeling to ‘understand’ personal preferences of mHealth users: a case study. In 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Vol. 1. Atlantis Press. 2019. p. 558-565 https://doi.org/10.2991/eusflat-19.2019.77