DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks
Year: 2019
Type: conference-paper
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.
Authors: Marco Maier, Daniel Elsner, Chadly Marouane, Meike Zehnle, Christoph Fuchs
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W2945728982