The work describes an efficient model to detect negative mind states caused by visual analytics tasks. We have developed a method for collecting data from multiple sensors, including GSR and eye-tracking, and quickly generating labelled training data for the machine learning model. Using this method we have created a dataset from 28 participants carrying out intentionally difficult visualization tasks. We have concluded the paper by discussing the best performing model, Random Forest, and its future applications for providing just-in-time assistance for visual analytics.
[pods name="publication" id="4215" template="Publication Template (list item)" shortcodes=1]
[pods name="publication" id="4218" template="Publication Template (list item)" shortcodes=1]