Exploring the Contribution of Isochrony-based Features to Computerized Assessment of Handwriting Disabilities
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | 45th International Conference on Telecommunications and Signal Processing (TSP) |
| MU Faculty or unit | |
| Citation | |
| web | https://ieeexplore.ieee.org/document/9851254 |
| Doi | http://dx.doi.org/10.1109/TSP55681.2022.9851254 |
| Keywords | Analytical models; Estimation error; Databases; Computational modeling; Machine learning; Signal processing; Predictive models |
| Description | Approximately 30–60 % of the time children spend in school is associated with handwriting. However, up to 30 % of them experience handwriting disabilities (HD), which lead to a decrease in their academic performance. Current HD assessment methods are not unified and show signs of subjectivity which can lead to misdiagnosis. The aim of this paper is to propose a new approach to objective HD assessment based on the principle of movement isochrony. For this purpose, we used a database of 137 children attending a primary school, who performed a transcription and dictation task, and who were associated with a BHK (Concise Evaluation Scale for Children's Handwriting) score. Employing a machine learning model, we were able to estimate this score with 18 % error. An interpretation of the model suggests that the isochrony-based features could bring new benefits to the objective assessment of HD. |
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