Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application
Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/13/3/598 |
| _version_ | 1797610679342989312 |
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| author | Eugenio Lomurno Linda Greta Dui Madhurii Gatto Matteo Bollettino Matteo Matteucci Simona Ferrante |
| author_facet | Eugenio Lomurno Linda Greta Dui Madhurii Gatto Matteo Bollettino Matteo Matteucci Simona Ferrante |
| author_sort | Eugenio Lomurno |
| collection | DOAJ |
| description | Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals’ academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify “at-risk” children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques. |
| first_indexed | 2024-03-11T06:17:39Z |
| format | Article |
| id | doaj.art-cdae2f04436142b896e9604f4d11a860 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-11T06:17:39Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-cdae2f04436142b896e9604f4d11a8602023-11-17T12:09:45ZengMDPI AGLife2075-17292023-02-0113359810.3390/life13030598Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet ApplicationEugenio Lomurno0Linda Greta Dui1Madhurii Gatto2Matteo Bollettino3Matteo Matteucci4Simona Ferrante5Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals’ academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify “at-risk” children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques.https://www.mdpi.com/2075-1729/13/3/598dysgraphialongitudinal monitoringearly screeningtime series embeddingprocrustes analysisdeep learning |
| spellingShingle | Eugenio Lomurno Linda Greta Dui Madhurii Gatto Matteo Bollettino Matteo Matteucci Simona Ferrante Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application Life dysgraphia longitudinal monitoring early screening time series embedding procrustes analysis deep learning |
| title | Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application |
| title_full | Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application |
| title_fullStr | Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application |
| title_full_unstemmed | Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application |
| title_short | Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application |
| title_sort | deep learning and procrustes analysis for early dysgraphia risk detection with a tablet application |
| topic | dysgraphia longitudinal monitoring early screening time series embedding procrustes analysis deep learning |
| url | https://www.mdpi.com/2075-1729/13/3/598 |
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