Digital Tools for Handwriting Proficiency Evaluation in Children

Termine

et al. 2023

Sensors

Self Cite

Dysgraphia is a learning disability that causes handwritten production below expectations. Its diagnosis is delayed until the completion of handwriting development. To allow a preventive training program, abilities not directly related to handwriting should be evaluated, and one of them is visual perception. To investigate the role of visual perception in handwriting skills, we gamified standard clinical visual perception tests to be played while wearing an eye tracker at three difficulty levels. Then, we identified children at risk of dysgraphia through the means of a handwriting speed test. Five machine learning models were constructed to predict if the child was at risk, using the CatBoost algorithm with Nested Cross-Validation, with combinations of game performance, eye-tracking, and drawing data as predictors. A total of 53 children participated in the study. The machine learning models obtained good results, particularly with game performances as predictors (F1 score: 0.77 train, 0.71 test). SHAP explainer was used to identify the most impactful features. The game reached an excellent usability score (89.4 ± 9.6). These results are promising to suggest a new tool for dysgraphia early screening based on visual perception skills.

Section: Discussionmentioning

confidence: 99%

Investigating Visual Perception Impairments through Serious Games and Eye Tracking to Anticipate Handwriting Difficulties

Piazzalunga

Termine

et al. 2023

Sensors

Self Cite

“…The latter, in fact, is delayed since it is based on the assessment of the speed and quality of the final handwritten text, when handwriting is mastered: it thus relies on a subjective evaluation of the final product. Moreover, it can also be further delayed by an often expensive and difficult to be reached professional consultation [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

“… The strong dependence on the native language. The necessity to depend only on handwritten text, and thus without providing a quantitative assessment of gestural production [ 10 ]. The requirement for children to reach the clinicians’ office, which may not be easily accessible in terms of costs and waiting time [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Lomurno

Gatto

et al. 2023

Life

Self Cite

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.

“…On the other hand, implementing preventive training programs for entire classrooms is not feasible, as they are time consuming and costly due to teachers' training.To overcome these limits, novel screening approaches must include the possibility to objectively study the learning trajectory of children from an early stage, even in a remote setting. The digitalization of dysgraphia diagnosis has been a trending topic during the last years [9][10][11][12] . The standard approach includes a digital tool-such as, a digitizer or a tablet equipped with a digital pen, or a smart ink pen to write directly on paper 13 -capable of collecting information about pen pressure and tilt.…”

mentioning

confidence: 99%

Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage

Lomurno²,

Lunardini³

et al. 2022

Sci Rep

Self Cite

Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children’s lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems, from a pre-literacy stage, based on drawings instead of words production analysis. Two hundred forty-one kindergartners drew on a tablet, and we computed features known to be distinctive of poor handwriting from symbols drawings. We verified that abnormal features patterns reflected abnormal drawings, and found correspondence in experts’ evaluation of the potential risk of developing a learning delay in the graphical sphere. A machine learning model was able to discriminate with 0.75 sensitivity and 0.76 specificity children at risk. Finally, we explained why children were considered at risk by the algorithms to inform teachers on the specific weaknesses that need training. Thanks to this system, early intervention to train specific learning delays will be finally possible.