Diagnosis of a specific learning disability such as dysgraphia impacts children's academic progress and well-being. Dysgraphia is diagnosed by clinicians based on children's written product and educational staff's impressions. This process is time consuming and subjective. Consequently, many children with mild dysgraphia remain undiagnosed, especially those from lower socioeconomic backgrounds. In this work, a method for automatic identification and characterization of dysgraphia in third-grade children is described. The method is based on analyzing the child's writing dynamics by sampling the pressure the pen exerts on the paper as well as the pen's position and orientation by using a standard digital writing pad. Ninety-nine samples were collected from writers with dysgraphia and proficient writers. A wide range of features covering dynamic properties of the writing and typographic (i.e., visual) properties were extracted for each participant. Machine learning methodologies were used to infer a statistical model, which is capable of discriminating dysgraphic products from proficient products with approximately 90% accuracy. The model was analyzed to conclude which handwriting features are most discriminative. Since the model provides 90% sensitivity for a specificity of 90%, it is the first step toward future use as an effective standard indicator for dysgraphia detection.

OBJECTIVE. This study was used to (1) develop an occupational therapy screening questionnaire (the Handwriting Proficiency Screening Questionnaire [HPSQ]) to identify handwriting difficulties among school-aged children and (2) examine its reliability and validity. METHOD. The questionnaire’s content validity was established. Internal consistency, interrater and test–retest reliability, and concurrent and construct validity were initiated. Participants included 7-to 14-year-old ( N = 230) typically developing school-aged children. RESULTS. The tool demonstrated good internal consistency (α = .90). Test–retest reliability for the score revealed an intraclass correlation coefficient (ICC) of .84 and interrater reliability of ICC =.92 for the total questionnaire score. Construct and concurrent validity were also confirmed. CONCLUSION. The HPSQ is suitable for use by occupational therapists for identification of handwriting deficiency among school-aged children and is appropriate for varied academic and clinical uses. More studies with larger samples of varied age groups are required to further support the questionnaire’s reliability and validity.

Demands for the production of legible handwriting produced in a timely manner increase as children progress through school. Despite the considerable number of children faced with handwriting difficulties, there is no quick and practical tool to assess legibility in this population. The aim of this study was to develop the Handwriting Legibility Scale (HLS) and to establish the scale's reliability and validity. The HLS is a non-language dependent scale which assesses global legibility, based on five criteria applied to samples of 'free writing'. Content validity, inter-rater reliability and internal consistency were initially examined using scripts from 20 children aged 8-14 years. Construct validity was established by Principal Component Analysis (PCA) of data from 150 school children aged 9-14 years, with an additional examination of gender effects. Discriminant validity of the HLS score was examined in 29 children with Developmental Coordination Disorder (DCD) and 29 typically developing (TD) children. The results indicated high internal consistency (α=0.92). The inter-rater reliability was acceptable but indicates the need to refine the scoring instructions. PCA of the five components revealed a one factor solution explaining 61% of the variance. In the discriminant analysis, 86.2% of the children with DCD and 89.7% of the TD children were correctly classified to their groups based on the total HLS score. The overall findings suggest that the HLS may be a useful tool to identify poor handwriting legibility, with application across different languages and writing scripts.