Mental Workload Classification via Online Writing Features

Mental workload is an important factor during writing, which may affect the writing efficiency and user experience. This paper aims at a method to classify the mental workload levels during writing process, via examination of online writing features in a two-stage algorithm structure. At the first stage, a curvature tracking method is applied to the handwriting script, to examine the curvature for individual writing points. Then a selection process allocates writing points into subsets, each corresponding to one curvature span. The second stage extracts velocity features, used to characterize mental workload, from points in each curvature span. A Parzen-window classifier is applied on velocity features from each curvature span. The classification decisions from individual classifiers are fused with a selective voting scheme for the overall mental workload classification decision. This paper finally discusses the classification accuracy for three mental workload levels and compares it with previous work.