EyeKnowYou: A DIY Toolkit to Support Monitoring Cognitive Load and Actual Screen Time using a Head-Mounted Webcam

Abstract

Studies show that frequent screen exposure and increased cognitive load can cause mental-health issues. Although expensive systems capable of detecting cognitive load and timers counting on-screen time exist, literature has yet to demonstrate measuring both factors across devices. To address this, we propose an inexpensive DIY-approach using a single head-mounted webcam capturing the user's eye. By classifying camera feed using a 3D Convolutional Neural Network, we can determine increased cognitive load and actual screen time. This works because the camera feed contains corneal surface reflection, as well as physiological parameters that contain information on cognitive load. Even with a small data set, we were able to develop generalised models showing 70% accuracy. To increase the models' accuracy, we seek the community's help by contributing more raw data. Therefore, we provide an opensource software and a DIY-guide to make our toolkit accessible to human factors researchers without an engineering background.