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
어린이의 필기 장애(HWD)는 자신감과 학업 진행에 부정적인 영향을 미칩니다. HWD를 감지하는 것은 HWD가 있는 어린이를 위한 임상 또는 교육 개입을 향한 첫 번째 중요한 단계입니다. 디지털 태블릿이 필기 프로세스 정보를 자동으로 수집할 수 있는 기회를 제공했지만 현재까지 HWD를 자동으로 감지하는 방법은 여전히 어려운 과제입니다. 특히, 우리가 아는 한, 어린이의 중국어 HWD를 자동으로 감지하기 위해 기계 학습 알고리즘과 필기 프로세스 정보를 결합하는 잠재력에 대한 탐구는 없습니다. 격차를 해소하기 위해 먼저 샘플 데이터를 수집하는 실험을 수행한 후 일반적으로 사용되는 1가지 분류 알고리즘(의사결정 트리, 지원 벡터 머신(SVM), 인공 신경망, Naïve Bayesian 및 k-Nearest Neighbor)의 탐지 성능을 비교했습니다. HWD. 결과는 다음과 같습니다: (13) 소수의 어린이(39%)만이 중국 HWD를 가지고 있었고 불균형 데이터 세트(HWD 위험이 있는 어린이 261명 대 일반 어린이 2명)에 대한 각 분류 모델은 무작위 추측보다 더 나은 결과를 산출했습니다. , 이는 중국 HWD를 탐지하기 위해 분류 알고리즘을 사용할 가능성을 나타냅니다. (3) SVM 모델은 XNUMX가지 분류 모델 중에서 중국 HWD를 탐지하는 데 가장 좋은 성능을 보였습니다. (XNUMX) 클래스 불균형 데이터를 처리하기 위해 합성 소수 오버샘플링 기술을 사용하면 SVM 모델의 성능, 특히 민감도가 크게 향상될 수 있습니다. 이 연구는 필기 특징이 어린이의 중국 HWD를 예측하는 방법에 대한 새로운 통찰력을 얻고 임상 및 교육 전문가가 중국 HWD 위험에 처한 어린이를 자동으로 감지하는 데 도움이 될 수 있는 방법을 제안합니다.
Zhiming WU
Sichuan University
Tao LIN
Sichuan University
Ming LI
Sichuan University
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부
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
부
@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},}
부
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 -