New Approach of Dysgraphic Handwriting Analysis Based on the Tunable Q-Factor Wavelet Transform.

ZVONČÁK, V, J MEKYSKA, Katarína ŠAFÁROVÁ, Z SMÉKAL a P BREZANY. New Approach of Dysgraphic Handwriting Analysis Based on the Tunable Q-Factor Wavelet Transform. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). Opatja, Chorvatsko, 2019, s. 289-294. ISBN 978-953-233-098-4.

Základní údaje

• Originální název: New Approach of Dysgraphic Handwriting Analysis Based on the Tunable Q-Factor Wavelet Transform.
• Autoři: ZVONČÁK, V, J MEKYSKA, Katarína ŠAFÁROVÁ, Z SMÉKAL a P BREZANY.
• Vydání: Opatja, Chorvatsko, 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), od s. 289-294, 2019.

Další údaje

• Typ výsledku: Stať ve sborníku
• Utajení: není předmětem státního či obchodního tajemství
• ISBN: 978-953-233-098-4
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

Developmental dysgraphia is a neurodevelopmental disorder present in up to 30% of elementary school pupils. Since it is associated with handwriting difficulties (HD), it has detrimental impact on children’s academic progress, emotional well-being, attitude and behaviour. Nowadays, researchers proposed a new approach of HD assessment utilizing digitizing tablets. I.e. that handwriting of children is quantified by a set of conventional parameters, such as velocity, duration of handwriting, tilt, etc. The aim of this study is to explore a potential of newly designed online handwriting features based on the tunable Q-factor wavelet transform (TQWT) in terms of computerized HD identification. Using a digitizing tablet, we recorded a written paragraph of 97 children who were also assessed by the Handwriting Proficiency Screening Questionnaire for Children (HPSQ–C). We evaluated discrimination power (binary classification) of all parameters using random forest and support vector machine classifiers in combination with sequential floating forward feature selection. Based on the experimental results we observed that the newly designed features outperformed the conventional ones (accuracy = 79.16%, sensitivity = 86.22%, specificity = 73.32%). When considering the combination of all parameters (including the conventional ones) we reached 84.66% classification accuracy (sensitivity = 88.70%, specificity = 82.53%). The most discriminative parameters were based on vertical movement and pressure, which suggests that children with HD were not able to maintain stable force on pen tip and that their vertical movement is less fluent. The new features we introduced go beyond the state-of-the-art and improve discrimination power of the conventional parameters by approximately 20.0%.

Anotace anglicky

Developmental dysgraphia is a neurodevelopmental disorder present in up to 30% of elementary school pupils. Since it is associated with handwriting difficulties (HD), it has detrimental impact on children’s academic progress, emotional well-being, attitude and behaviour. Nowadays, researchers proposed a new approach of HD assessment utilizing digitizing tablets. I.e. that handwriting of children is quantified by a set of conventional parameters, such as velocity, duration of handwriting, tilt, etc. The aim of this study is to explore a potential of newly designed online handwriting features based on the tunable Q-factor wavelet transform (TQWT) in terms of computerized HD identification. Using a digitizing tablet, we recorded a written paragraph of 97 children who were also assessed by the Handwriting Proficiency Screening Questionnaire for Children (HPSQ–C). We evaluated discrimination power (binary classification) of all parameters using random forest and support vector machine classifiers in combination with sequential floating forward feature selection. Based on the experimental results we observed that the newly designed features outperformed the conventional ones (accuracy = 79.16%, sensitivity = 86.22%, specificity = 73.32%). When considering the combination of all parameters (including the conventional ones) we reached 84.66% classification accuracy (sensitivity = 88.70%, specificity = 82.53%). The most discriminative parameters were based on vertical movement and pressure, which suggests that children with HD were not able to maintain stable force on pen tip and that their vertical movement is less fluent. The new features we introduced go beyond the state-of-the-art and improve discrimination power of the conventional parameters by approximately 20.0%.

Návaznosti

• GA18-16835S, projekt VaV: Název: Výzkum pokročilých metod diagnózy a hodnocení vývojové dysgrafie založených na kvantitativní analýze online písma a kresby (Akronym: DiagnosisDysgraphia)

Investor: Grantová agentura ČR, Výzkum pokročilých metod diagnózy a hodnocení vývojové dysgrafie založených na kvantitativní analýze online písma a kresby