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

The affective state called flow is described as a state of optimal experience, total immersion and high productivity. As an important metric for various scenarios ranging from (professional) sports to work environments to user experience evaluations, it is extensively studied using traditional questionnaires. In order to make flow measurement accessible for online, real-time environments, in this work, we present our preliminary findings towards automatically estimating a user's flow state based on physiological signals measured with a wearable device. We conducted a study of subjects playing the game Tetris in varying difficulty levels, leading to boredom, stress, and flow. Using a convolutional neural network, we achieve an accuracy of 70% in recognizing flow-inducing levels. In the future, we expect flow to be a potential reward signal for human-in-the-loop reinforcement learning systems.