DeepFlow
- Author(s)
- Marco Maier, Daniel Elsner, Chadly Marouane, Meike Zehnle, Christoph Fuchs
- Abstract
Flow is an affective state of optimal experience, total immersion and high productivity. While often associated with (professional) sports, it is a valuable information in several scenarios ranging from work environments to user experience evaluations, and we expect it to be a potential reward signal for human-in-the-loop reinforcement learning systems. Traditionally, flow has been assessed through questionnaires which prevents its use in online, real-time environments. In this work, we present our findings towards estimating a user's flow state based on physiological signals measured using wearable devices. We conducted a study with participants playing the game Tetris in varying difficulty levels, leading to boredom, stress, and flow. Using an end-to-end deep learning architecture, we achieve an accuracy of 67.50% in recognizing high flow vs. low flow states and 49.23% in distinguishing all three affective states boredom, flow, and stress.
- Organisation(s)
- External organisation(s)
- Technische Universität München
- Pages
- 1415-1421
- No. of pages
- 7
- DOI
- https://doi.org/10.24963/ijcai.2019/196
- Publication date
- 2019
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502019 Marketing
- Keywords
- ASJC Scopus subject areas
- Artificial Intelligence
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/922a8972-e64d-4fa9-9992-2da4a11d7468