Classification techniques for handwriting difficulties among children in early stage of academic life
In today's era, all aspects of complex occupational task, plus the importance of early identification of developmental disorders in children, demand the essential need for screening children’s handwriting at elementary schools. Many underlying competence structures may interfere with handwritin...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/54076/1/AnithAdibahHasseimMFKE2015.pdf |
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| Summary: | In today's era, all aspects of complex occupational task, plus the importance of early identification of developmental disorders in children, demand the essential need for screening children’s handwriting at elementary schools. Many underlying competence structures may interfere with handwriting performance. Children starting their academic programme should be tested for their handwriting abilities and readiness through regular routine screening. Screening a vast majority of 4 to 7+ years old necessitate the use of automated systems to collect data, keep tracks, and increase the speed of analysis and accuracy. Based on Handwriting Proficiency Screening Questionnaire (HSPQ) evaluated by their teachers, 120 pupils were individually tested on their use of graphic production rules. Then, the samples were divided into two group of writers; below average writers (test group) and above average writers (control group) based on the score of HSPQ. Each participant was required to copy four basic lines in two opposite directions and trace a sequence of rotated semi circles. This research examines the dynamic features such as ratio of time taken and standard deviation of pen pressure. In this study, three classification methods: Artificial Neural Network, Logistic Regression and Support Vector Machine (SVM) were chosen to classify children with handwriting problem. 10-fold cross-validation method is used for testing and training. At the end of this study, the results among these classifiers and features were compared. Based on the results, it can be concluded that the performance of SVM with Radial Basis Function kernel is the best among classifiers as it gives 100% of screening accuracy. |
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