Mouse-Cursor Tracking: Simple Scoring Algorithms That Make it Work

Abstract

Mouse-cursor tracking, a new action-based measure of behavior, has emerged as one of the promising applications of affective computing. As facial expressions, gaits, electroencephalogram (EEG), and electrodermal activity (EDA) inform the emotions of computer users, the movement of the computer mouse-cursor reveals when people feel anxious, relaxed, attentive, joyful, and sad. However, the mouse tracking analysis has not previously been subject to systematic investigations of psychometric properties. The choice of motor features, experimental manipulations, and data transformation methods is ad hoc. In this study, we evaluate the impact of psychological factors on mouse-based affective computing and propose simple scoring algorithms that incorporate psychometric features such as the frame of reference, habituation, and measurement error. Our results demonstrate that our new dimensionality reduction method, merged PCA, outperforms conventional procedures, improving prediction performance by about $15-30\%$.