Mouse Trajectories and State Anxiety: Feature Selection with Random Forest
Additional Document Info
Do users' mouse activities reveal their affective states, as other bodily expressions such as postures and gestures signal emotions? When people are frustrated while trying to solve a puzzle or math problem, their frustration can be manifested in the way they use a computer mouse, such as pressing a button hard. But when a user is engaged in an innocuous and mundane task, what mouse activities provide a clue to detect affective states? To address these questions, we extracted 134 mouse trajectory variables in a choice-reaching experiment (N=234, female = 137, male = 97) and selected 3~8 key features by applying random forest regression. Using Spielberger's State Anxiety Inventory, we investigated the extent to which the selected trajectory features predict state anxiety of new subjects (N = 133, female = 75, male = 58). Results indicate that distributions of temporal features (e.g., velocity) as well as spatial characteristics (e.g., direction change) are indicative of users' state anxiety. A theoretical rationale, pros and cons of using mouse movement analysis and the role of other psychological variable for mouse-based affective computing are also discussed. 2013 IEEE.
name of conference
2013 Humaine Association Conference on Affective Computing and Intelligent Interaction