Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application

Lomurno, Eugenio; Dui, Linda Greta; Gatto, Madhurii; Bollettino, Matteo; Matteucci, Matteo; Ferrante, Simona

doi:10.3390/life13030598

Open AccessArticle

Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application

by

Eugenio Lomurno

^*,†

,

Linda Greta Dui

^†

,

Madhurii Gatto

^†

,

Matteo Bollettino

^†

,

Matteo Matteucci

and

Simona Ferrante

Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Life 2023, 13(3), 598; https://doi.org/10.3390/life13030598

Submission received: 30 December 2022 / Revised: 13 February 2023 / Accepted: 17 February 2023 / Published: 21 February 2023

(This article belongs to the Special Issue Mathematical Methods and Data Analysis in Health and Biomedical Sciences)

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Abstract

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.

Keywords: dysgraphia; longitudinal monitoring; early screening; time series embedding; procrustes analysis; deep learning

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MDPI and ACS Style

Lomurno, E.; Dui, L.G.; Gatto, M.; Bollettino, M.; Matteucci, M.; Ferrante, S. Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. Life 2023, 13, 598. https://doi.org/10.3390/life13030598

AMA Style

Lomurno E, Dui LG, Gatto M, Bollettino M, Matteucci M, Ferrante S. Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. Life. 2023; 13(3):598. https://doi.org/10.3390/life13030598

Chicago/Turabian Style

Lomurno, Eugenio, Linda Greta Dui, Madhurii Gatto, Matteo Bollettino, Matteo Matteucci, and Simona Ferrante. 2023. "Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application" Life 13, no. 3: 598. https://doi.org/10.3390/life13030598

APA Style

Lomurno, E., Dui, L. G., Gatto, M., Bollettino, M., Matteucci, M., & Ferrante, S. (2023). Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. Life, 13(3), 598. https://doi.org/10.3390/life13030598

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Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application

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