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

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.