Affective Computing for detecting psychological Flow state: A definition and methodological problem

Elena Sajno

Research output: Contribution to journalConference article

Abstract

Flow is a mental state connected to optimal experiences and high performance. Existing detection systems are limited to post-hoc or require repeated and distracting assessments. Affective Computing offers the potential to be a viable framework for its detection and characterization. The formalization of such a model depends, however, on reliable assessment, elicitation, and detection of Flow. To this end, this work proposes that 1) Flow can be charted as a high valence, high arousal, and high dominance state: concordance of results with traditional evaluation scales (e.g., Flow State Scale) would be checked to confirm the validity of the assessment methods. 2) A video game, with difficulties tailored to the subject's performance, can elicit Flow, as well as Engagement, boredom, or anxiety. 3) Specific physiological correlates (i.e., ECG and EDA) can be leveraged for its detection.
Original languageEnglish
Pages (from-to)1-5
Number of pages5
Journal11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2023
Volume2023
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2023 - usa
Duration: 10 Sept 202313 Sept 2023

Keywords

  • Affective Computing
  • ECG
  • EDA
  • Engagement
  • Sensors
  • HRV
  • Machine Learning
  • Positive Psychology
  • Flow

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