Abstract
| Original language | English |
|---|---|
| Title of host publication | 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) |
| Publisher | Atlantis Press |
| Pages | 558-565 |
| Number of pages | 8 |
| Volume | 1 |
| ISBN (Electronic) | 978-94-6252-770-6 |
| DOIs | |
| Publication status | Published - Sep 2019 |
| Event | 11th Conference of the European Society for Fuzzy Logic and Technology - Prague, Czech Republic Duration: 9 Sep 2019 → 13 Sep 2019 |
Conference
| Conference | 11th Conference of the European Society for Fuzzy Logic and Technology |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 9/09/19 → 13/09/19 |
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Keywords
- fuzzy inference system
- Takagi-Sugeno
- personalization
- mHealth
Cite this
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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 proceeding › Conference contribution › Academic › peer-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 -