Automatizovaná diagnóza vývojové dysgrafie založená na kvantitativní analýze online písma
| Title in English | Diagnosis of Developmental Dysgraphia Based on Quantitative Analysis of Online Handwriting |
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| Authors | |
| Year of publication | 2018 |
| Type | Article in Periodical |
| Magazine / Source | Elektrorevue |
| MU Faculty or unit | |
| Citation | |
| Web | http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/automatizovana-diagnoza-vyvojove-dysgrafie-zalozena-na-kvantitativni-analyze-online-pisma-1/ |
| Keywords | developmental dysgraphia; children dysgraphia; digitizing tablet; HPSQ; random forests; support vector machine |
| Description | The prevalence of handwriting difficulties among school-aged children is around 10–30 %. Until now, there is no objective method to diagnose and rate developmental dysgraphia (DD) in Czech Republic. The goal of this study is to propose a new method of objective DD diagnosis based on quantitative analysis of online handwriting. For this purpose, we extracted a set of spatial, temporal, kinematic and dynamic features from three handwriting tasks. Consequently, we performed a correlation analysis between these features and score of handwriting proficiency screening questionnaire (HPSQ), in order to identify parameters with a good discrimination power. Using random forests classifier in combination with quantification of alphabet writing task, we reached nearly 80% classification accuracy (77% sensitivity, 83% specificity). The classification accuracy was increased to 92% (92% sensitivity, 93% specificity) by employing SFFS (Sequential Forward Feature Selection) method. This pilot study proves the possibility of automatic DD diagnosis in children cohort writing with cursive letters. |
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