Handwriting Evaluation Using Deep Learning with SensoGrip.
Sensors (Basel)
; 23(11)2023 May 31.
Article
in En
| MEDLINE
| ID: mdl-37299942
ABSTRACT
Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children's academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using machine learning algorithms with a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification either dysgraphia or no dysgraphia. In this work, we investigated the fine grading of handwriting capabilities by predicting the SEMS score (between 0 and 12) with deep learning. Our approach provided a root-mean-square error of less than 1 with automatic instead of manual feature extraction and selection. Furthermore, the SensoGrip smart pen SensoGrip was used, i.e., a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Agraphia
/
Deep Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Child
/
Humans
Language:
En
Journal:
Sensors (Basel)
Year:
2023
Document type:
Article
Affiliation country:
Austria