Optimisation procedures for diagnostic processing of hand-drawn geometric figures

An investigation into task and feature selection for analysing diagnostic drawings using handwriting dynamics and image processing techniques is presented. Performance across a standard test battery and a subset selection process for both features and tasks is examined. It is shown that a reduced task domain provides results which can identify trends in patient performance at a higher accuracy than is obtainable when the whole feature/task set is used.