  The detection of negative emotions through daily activities such as
handwriting is useful for promoting well-being. The spread of human-machine
interfaces such as tablets makes the collection of handwriting samples easier.
In this context, we present a first publicly available handwriting database
which relates emotional states to handwriting, that we call EMOTHAW. This
database includes samples of 129 participants whose emotional states, namely
anxiety, depression and stress, are assessed by the Depression Anxiety Stress
Scales (DASS) questionnaire. Seven tasks are recorded through a digitizing
tablet: pentagons and house drawing, words copied in handprint, circles and
clock drawing, and one sentence copied in cursive writing. Records consist in
pen positions, on-paper and in-air, time stamp, pressure, pen azimuth and
altitude. We report our analysis on this database. From collected data, we
first compute measurements related to timing and ductus. We compute separate
measurements according to the position of the writing device: on paper or
in-air. We analyse and classify this set of measurements (referred to as
features) using a random forest approach. This latter is a machine learning
method [2], based on an ensemble of decision trees, which includes a feature
ranking process. We use this ranking process to identify the features which
best reveal a targeted emotional state.
  We then build random forest classifiers associated to each emotional state.
Our results, obtained from cross-validation experiments, show that the targeted
emotional states can be identified with accuracies ranging from 60% to 71%.
