@inproceedings{75bbd7a3a8004a59b96442fa444e1ca3,
title = "Fuzzy modeling to {\textquoteleft}understand{\textquoteright} 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, MHealth, Personalization, Takagi-Sugeno",
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 = sep,
doi = "10.2991/eusflat-19.2019.77",
language = "English",
series = "Atlantis Studies in Uncertainty Modelling",
publisher = "Atlantis Press",
pages = "558--565",
editor = "Vilem Novak and Vladimir Marik and Martin Stepnicka and Mirko Navara and Petr Hurtik",
booktitle = "2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)",
address = "Netherlands",
note = "11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 ; Conference date: 09-09-2019 Through 13-09-2019",
}