The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kesukaran tulisan tangan (HWD) pada kanak-kanak mempunyai kesan buruk terhadap keyakinan dan kemajuan akademik mereka. Mengesan HWD adalah langkah penting pertama ke arah intervensi klinikal atau pengajaran untuk kanak-kanak dengan HWD. Sehingga kini, cara untuk mengesan HWD secara automatik masih menjadi cabaran, walaupun pendigitan tablet telah memberi peluang untuk mengumpul maklumat proses tulisan tangan secara automatik. Terutamanya, untuk pengetahuan terbaik kami, tiada penerokaan ke dalam potensi menggabungkan algoritma pembelajaran mesin dan maklumat proses tulisan tangan untuk mengesan HWD Cina secara automatik pada kanak-kanak. Untuk merapatkan jurang, kami mula-mula menjalankan eksperimen untuk mengumpul data sampel dan kemudian membandingkan prestasi lima algoritma klasifikasi yang biasa digunakan (Pokok keputusan, Mesin Vektor Sokongan (SVM), Rangkaian Neural Buatan, Naïve Bayesian dan k-Nearest Neighbor) dalam mengesan HWD. Keputusan menunjukkan bahawa: (1) hanya sebahagian kecil (13%) kanak-kanak yang mempunyai HWD Cina dan setiap model klasifikasi pada dataset tidak seimbang (39 kanak-kanak berisiko HWD berbanding 261 kanak-kanak biasa) menghasilkan keputusan yang lebih baik daripada tekaan rawak , menunjukkan kemungkinan menggunakan algoritma pengelasan untuk mengesan HWD Cina; (2) model SVM mempunyai prestasi terbaik dalam mengesan HWD Cina antara lima model klasifikasi; dan (3) prestasi model SVM, terutamanya sensitivitinya, boleh dipertingkatkan dengan ketara dengan menggunakan Teknik Pensampelan Lebihan Minoriti Sintetik untuk mengendalikan data ketidakseimbangan kelas. Kajian ini memperoleh pandangan baharu tentang ciri tulisan tangan yang meramalkan HWD Cina dalam kalangan kanak-kanak dan mencadangkan kaedah yang boleh membantu profesional klinikal dan pendidikan untuk mengesan kanak-kanak yang berisiko HWD Cina secara automatik.
Zhiming WU
Sichuan University
Tao LIN
Sichuan University
Ming LI
Sichuan University
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Salinan
Zhiming WU, Tao LIN, Ming LI, "Automated Detection of Children at Risk of Chinese Handwriting Difficulties Using Handwriting Process Information: An Exploratory Study" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 147-155, January 2019, doi: 10.1587/transinf.2017EDP7224.
Abstract: Handwriting difficulties (HWDs) in children have adverse effects on their confidence and academic progress. Detecting HWDs is the first crucial step toward clinical or teaching intervention for children with HWDs. To date, how to automatically detect HWDs is still a challenge, although digitizing tablets have provided an opportunity to automatically collect handwriting process information. Especially, to our best knowledge, there is no exploration into the potential of combining machine learning algorithms and the handwriting process information to automatically detect Chinese HWDs in children. To bridge the gap, we first conducted an experiment to collect sample data and then compared the performance of five commonly used classification algorithms (Decision tree, Support Vector Machine (SVM), Artificial Neural Network, Naïve Bayesian and k-Nearest Neighbor) in detecting HWDs. The results showed that: (1) only a small proportion (13%) of children had Chinese HWDs and each classification model on the imbalanced dataset (39 children at risk of HWDs versus 261 typical children) produced the results that were better than random guesses, indicating the possibility of using classification algorithms to detect Chinese HWDs; (2) the SVM model had the best performance in detecting Chinese HWDs among the five classification models; and (3) the performance of the SVM model, especially its sensitivity, could be significantly improved by employing the Synthetic Minority Oversampling Technique to handle the class-imbalanced data. This study gains new insights into which handwriting features are predictive of Chinese HWDs in children and proposes a method that can help the clinical and educational professionals to automatically detect children at risk of Chinese HWDs.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7224/_p
Salinan
@ARTICLE{e102-d_1_147,
author={Zhiming WU, Tao LIN, Ming LI, },
journal={IEICE TRANSACTIONS on Information},
title={Automated Detection of Children at Risk of Chinese Handwriting Difficulties Using Handwriting Process Information: An Exploratory Study},
year={2019},
volume={E102-D},
number={1},
pages={147-155},
abstract={Handwriting difficulties (HWDs) in children have adverse effects on their confidence and academic progress. Detecting HWDs is the first crucial step toward clinical or teaching intervention for children with HWDs. To date, how to automatically detect HWDs is still a challenge, although digitizing tablets have provided an opportunity to automatically collect handwriting process information. Especially, to our best knowledge, there is no exploration into the potential of combining machine learning algorithms and the handwriting process information to automatically detect Chinese HWDs in children. To bridge the gap, we first conducted an experiment to collect sample data and then compared the performance of five commonly used classification algorithms (Decision tree, Support Vector Machine (SVM), Artificial Neural Network, Naïve Bayesian and k-Nearest Neighbor) in detecting HWDs. The results showed that: (1) only a small proportion (13%) of children had Chinese HWDs and each classification model on the imbalanced dataset (39 children at risk of HWDs versus 261 typical children) produced the results that were better than random guesses, indicating the possibility of using classification algorithms to detect Chinese HWDs; (2) the SVM model had the best performance in detecting Chinese HWDs among the five classification models; and (3) the performance of the SVM model, especially its sensitivity, could be significantly improved by employing the Synthetic Minority Oversampling Technique to handle the class-imbalanced data. This study gains new insights into which handwriting features are predictive of Chinese HWDs in children and proposes a method that can help the clinical and educational professionals to automatically detect children at risk of Chinese HWDs.},
keywords={},
doi={10.1587/transinf.2017EDP7224},
ISSN={1745-1361},
month={January},}
Salinan
TY - JOUR
TI - Automated Detection of Children at Risk of Chinese Handwriting Difficulties Using Handwriting Process Information: An Exploratory Study
T2 - IEICE TRANSACTIONS on Information
SP - 147
EP - 155
AU - Zhiming WU
AU - Tao LIN
AU - Ming LI
PY - 2019
DO - 10.1587/transinf.2017EDP7224
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - Handwriting difficulties (HWDs) in children have adverse effects on their confidence and academic progress. Detecting HWDs is the first crucial step toward clinical or teaching intervention for children with HWDs. To date, how to automatically detect HWDs is still a challenge, although digitizing tablets have provided an opportunity to automatically collect handwriting process information. Especially, to our best knowledge, there is no exploration into the potential of combining machine learning algorithms and the handwriting process information to automatically detect Chinese HWDs in children. To bridge the gap, we first conducted an experiment to collect sample data and then compared the performance of five commonly used classification algorithms (Decision tree, Support Vector Machine (SVM), Artificial Neural Network, Naïve Bayesian and k-Nearest Neighbor) in detecting HWDs. The results showed that: (1) only a small proportion (13%) of children had Chinese HWDs and each classification model on the imbalanced dataset (39 children at risk of HWDs versus 261 typical children) produced the results that were better than random guesses, indicating the possibility of using classification algorithms to detect Chinese HWDs; (2) the SVM model had the best performance in detecting Chinese HWDs among the five classification models; and (3) the performance of the SVM model, especially its sensitivity, could be significantly improved by employing the Synthetic Minority Oversampling Technique to handle the class-imbalanced data. This study gains new insights into which handwriting features are predictive of Chinese HWDs in children and proposes a method that can help the clinical and educational professionals to automatically detect children at risk of Chinese HWDs.
ER -