DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks

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.