Computerised Assessment of Graphomotor Difficulties in a Cohort of Schoolaged Children Although graphomotor difficulties (GD) are present in up to 30% of schoolaged children, the field of GD diagnosis and assessment is not fully explored and several research gaps can be identified. This study aims to explore the impact of specific elementary graphomotor tasks analysis on the accuracy of computerised diagnosis and assessment of GD. We analysed seven basic graphomotor tasks from 76 children (assessed by special educational counsellors and using the handwriting proficiency screening questionnaire for children HPSQxe2x80x93C). Employing a differential analysis, we observed that the most discriminative tasks are based on combined loops, sawtooth and small Archimedean spiral drawings. Features with the highest discrimination power quantify kinematics, especially in the vertical projection. Using a multivariate mathematical model, we were able to identify GD with 50% sensitivity and 90% specificity, and to estimate the total score of HPSQxe2x80x93C with 31% error.

Fractional Order Derivatives Evaluation in Computerized Assessment of Handwriting Difficulties in Schoolaged Children Handwriting difficulties (HD) affects some of the schoolaged children and its current prevalence rate is between 5xe2x80x9334%. Children at primary schools have to face rising cognitive demands that the handwriting represents, and some of them are not able to do so. As a result, they tend to make mistakes and their written product is dysfluent and has poor legibility. HD can also lead them to lower selfesteem, learning difficulties and ultimately to less academic achievements. For this reason occupational therapists are trying to identify HD through examination as early as possible. We extracted online handwriting signals of children using digitizing tablets. Handwriting Proficiency Screening Questionnaire for Children (HPSQxe2x80x93C) was used to score severity of HD in childrenxe2x80x99s written product. To advance current computerized analysis of online handwriting, we employed fractional order derivative features (FD) together with conventional measures. We selected significant features for HD identification and utilized correlation analysis together with MannWhitney Utest to evaluate their discriminative power. We can conclude that FDbased features bring benefits of more robust quantification of inair movements as opposed to the conventionally used ones. Finally, we have shown that utilization of FD can be beneficial for computerized assessment of HD but should be further optimized and evaluated with advanced statistical or machine learning methods.