Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identify some factors and activities as emotional triggers, which were used for understanding the user context. Our results highlight the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicit user feedback.
|Title of host publication||Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||7|
|State||Published - 27 Jun 2020|
|Event||42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020 - Seoul, Korea, Republic of|
Duration: 27 Jun 2020 → 19 Jul 2020
|Name||Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020|
|Conference||42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020|
|Country/Territory||Korea, Republic of|
|Period||27/06/20 → 19/07/20|
Bibliographical noteFunding Information:
Special thanks go to the volunteer subject in the case study. This work has been supported by the projects: KUSISQA (014-2019-FONDECYT-BM-INC.INV) from the National Fund for Scientific and Technological Development (FONDECYT-PERU) and World Bank, and Datos 4.0 (TIN2016-78011-C4-1-R) supported by MINECO-AEI/FEDER-UE. Alejandro Catala is a Juan de la Cierva Researcher (IJC2018-037522-I). This research is partly funded by the Spanish Ministry of Science, Innovation and Universities (grant RTI2018-099646-B-I00) and the Galician Ministry of Education, University and Professional Training (grant ED431G2019/04). Some of the previous grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).
© 2020 ACM.
- Actionable emotion
- Case study
- Context information
- Implicit user feedback
- Physiological data