Synthesis of Handwriting Dynamics using Sinusoidal Model Handwriting production is a complex mechanism of fine motor control, associated with mainly two degrees of freedom in the horizontal and vertical directions. The relation between the horizontal and vertical velocities depends on the trajectory shape and its length. In this work, we explore the generation of handwriting velocities using two sinusoidal oscillations. The proposed method follows the motor equivalence theory and considers that the patterns are stored in the form of a sequence of corner shapes and its relative location in the letter. These points are referred to as the modulation points, where the parameters of the sinusoidal oscillations are modulated to generate required velocity profiles. Depending on the location and shape of the corners, the amplitude, phase, and frequency relations between the two underlying oscillations are changed. Accordingly, this paper presents an efficient method to synthesize the velocity profiles and hence the handwriting. Further, the shape variability in the synthesized data can also be introduced by modifying the position of the modulation points and its corner shapes. The quality of the synthesized handwriting is evaluated using both subjective and quantitative evaluation methods.

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.