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http://hdl.handle.net/20.500.12390/2535


Título: Understanding Implicit User Feedback from Multisensorial and Physiological Data: A case study
Autor(es): Suni-Lopez F. 
Condori-Fernandez N. 
Catala A. 
Resumen: 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. © 2020 ACM.
Tema: Actionable emotion;Case study;Context information;Implicit user feedback;Physiological data
Editorial: Association for Computing Machinery, Inc
Fecha de publicación: 2020
Publicado en: Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020 
Financiamiento: 014-2019-FONDECYT-BM 
Tipo de publicación: info:eu-repo/semantics/article
Identificador Handle: http://hdl.handle.net/20.500.12390/2535
DOI: 10.1145/3387940.3391466
Nivel de acceso: info:eu-repo/semantics/openAccess
Colección:6.1 Proyectos de investigación científica

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