Abstract
This paper proposes new ways to assess handwriting, a critical skill in any child’s school journey. Traditionally, a pen and paper test called the BHK test (Concise Evaluation Scale for Children’s Handwriting) is used to assess children’s handwriting in French-speaking countries. Any child with a BHK score above a certain threshold is diagnosed as ‘dysgraphic’, meaning that they are then eligible for financial coverage for therapeutic support. We previously developed a version of the BHK for tablet computers which provides rich data on the dynamics of writing (acceleration, pressure, and so forth). The underlying model was trained on dysgraphic and non-dysgraphic children. In this contribution, we deviate from the original BHK for three reasons. First, in this instance, we are interested not in a binary output but rather a scale of handwriting difficulties, from the lightest cases to the most severe. Therefore, we wish to compute how far a child’s score is from the average score of children of the same age and gender. Second, our model analyses dynamic features that are not accessible on paper; hence, the BHK is useful in this instance. Using the PCA (Principal Component Analysis) reduced the set of 53 handwriting features to three dimensions that are independent of the BHK. Nonetheless, we double-checked that, when clustering our data set along any of these three axes, we accurately detected dysgraphic children. Third, dysgraphia is an umbrella concept that embraces a broad variety of handwriting difficulties. Two children with the same global score can have totally different types of handwriting difficulties. For instance, one child could apply uneven pen pressure while another one could have trouble controlling their writing speed. Our new test not only provides a global score, but it also includes four specific score for kinematics, pressure, pen tilt and static features (letter shape). Replacing a global score with a more detailed profile enables the selection of remediation games that are very specific to each profile.
Highlights
Despite the entry of the educational system into the digital age, handwriting remains a central skill which must be acquired for every school child since it is still the basis of many core activities such as taking notes, composing stories and self-expression[1,2,3]
In the model of interest for this study, Asselborn et al.[21] used 53 features to describe handwriting that were sorted into four categories, namely, the static, kinematic, pressure and tilt categories
Every child involved in this study was asked to write the five first sentences used in the BHK test on a digital tablet
Summary
Despite the entry of the educational system into the digital age, handwriting remains a central skill which must be acquired for every school child since it is still the basis of many core activities such as taking notes, composing stories and self-expression[1,2,3]. Combining digital tablets with machine learning algorithms allows online and objective analyses of handwriting, which is far removed from the time- consuming, hand-written (and subjective) analyses done by a therapist For this reason, some digital tablet-based tests are slowly starting to appear[21,22,23]. In the model of interest for this study, Asselborn et al.[21] used 53 features to describe handwriting that were sorted into four categories, namely, the static (which can possibly be measured with a pen/paper test), kinematic, pressure and tilt (described in the Method Section) categories These features, designed in collaboration with therapists, were used as input into a Random Forest classifier, which detected severe handwriting difficulties (dysgraphia) with remarkable accuracy
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