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

DOI

10.2991/eusflat-19.2019.77How to use a DOI?

Keywords

fuzzy inference system Takagi-Sugeno personalization mHealth

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Raoul Nuijten
AU  - Uzay Kaymak
AU  - Pieter Van Gorp
AU  - Monique Simons
AU  - Pauline Van Den Berg
AU  - Pascale Le Blanc
PY  - 2019/08
DA  - 2019/08
TI  - Fuzzy modeling to 'understand' personal preferences of mHealth users: a case study
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 558
EP  - 565
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.77
DO  - 10.2991/eusflat-19.2019.77
ID  - Nuijten2019/08
ER  -