Using emotions to empower the self-adaptation capability of software services

Nelly Condori-Fernandez*, Franci Suni Lopez

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Traditional self-adaptive systems research has focused on external contextual aspects such as performance, system reaction to environment. In this paper, we introduce the idea of measuring emotions in order to empower the adaptability of software services at runtime. We present two type of monitoring mechanisms and an adaptive adaptation strategy, which were implemented as part of the HAPPYNESS middleware. A preliminary test using data from the Empatica repository was carried out with the purpose of assessing the goodness of the controller (i.e. inference engine), a component of our middleware. The obtained results were consistent with the expected values. Moreover, we test also the connectivity and synchronization between E4-Wristband and an adaptive mobile application that were used by two volunteer users.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering, SEmotion 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages15-21
Number of pages7
ISBN (Electronic)9781538627938
DOIs
Publication statusPublished - 28 Jun 2017
Event2nd IEEE/ACM International Workshop on Emotion Awareness in Software Engineering, SEmotion 2017 - Buenos Aires, Argentina
Duration: 21 May 2017 → …

Publication series

NameProceedings - 2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering, SEmotion 2017

Conference

Conference2nd IEEE/ACM International Workshop on Emotion Awareness in Software Engineering, SEmotion 2017
Country/TerritoryArgentina
CityBuenos Aires
Period21/05/17 → …

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • (self) adaptive services
  • E4-Wristband
  • medication adherence
  • physiological data
  • stress

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