This paper is in the following e-collection/theme issue:

Serious Games for Health and Medicine (683) Research Instruments, Questionnaires, and Tools (576) mHealth for Data Collection and Research (955) Diagnostic Tools in Mental Health (242) Developmental Problems (47)

Published on in Vol 8, No 4 (2020): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20126, first published .
A Tablet App for Handwriting Skill Screening at the Preliteracy Stage: Instrument Validation Study

A Tablet App for Handwriting Skill Screening at the Preliteracy Stage: Instrument Validation Study

A Tablet App for Handwriting Skill Screening at the Preliteracy Stage: Instrument Validation Study

Authors of this article:

Linda Greta Dui1 Author Orcid Image ;   Francesca Lunardini1 Author Orcid Image ;   Cristiano Termine2 Author Orcid Image ;   Matteo Matteucci1 Author Orcid Image ;   Natale Adolfo Stucchi3 Author Orcid Image ;   Nunzio Alberto Borghese4 Author Orcid Image ;   Simona Ferrante1 Author Orcid Image
Article Authors Cited by (25) Tweetations (6) Metrics

Journals

  1. Lunardini F, Febbo D, Malavolti M, Cid M, Serra M, Piccini L, Pedrocchi A, Borghese N, Ferrante S. A Smart Ink Pen for the Ecological Assessment of Age-Related Changes in Writing and Tremor Features. IEEE Transactions on Instrumentation and Measurement 2021;70:1 View
  2. Piazzalunga C, Dui L, Termine C, Bortolozzo M, Matteucci M, Ferrante S. Investigating Visual Perception Impairments through Serious Games and Eye Tracking to Anticipate Handwriting Difficulties. Sensors 2023;23(4):1765 View
  3. Milosevic M, Nakanishi T, Sasaki A, Yamaguchi A, Nomura T, Popovic M, Nakazawa K. Cortical Re-organization After Traumatic Brain Injury Elicited Using Functional Electrical Stimulation Therapy: A Case Report. Frontiers in Neuroscience 2021;15 View
  4. Lomurno E, Dui L, 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 View
  5. Guan Q, Wang J, Chen Y, Liu Y, He H. Beyond information rate, the capacity of cognitive control predicts response criteria in perceptual decision-making. Brain and Cognition 2021;154:105788 View
  6. Dui L, Lomurno E, Lunardini F, Termine C, Campi A, Matteucci M, Ferrante S. Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage. Scientific Reports 2022;12(1) View
  7. Khaleghi A, Aghaei Z, Behnamghader M. Developing two game-based interventions for dyslexia therapeutic interventions using gamification and serious games approaches entertainment computing journal. Entertainment Computing 2022;42:100482 View
  8. Kalenjuk E, Laletas S, Subban P, Wilson S. A scoping review to map research on children with dysgraphia, their carers, and educators. Australian Journal of Learning Difficulties 2022;27(1):19 View
  9. Kunhoth J, Al Maadeed S, Saleh M, Akbari Y. Exploration and analysis of On-Surface and In-Air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods. Biomedical Signal Processing and Control 2023;83:104715 View
  10. Laorden Gutiérrez C, Royo García P, Serrano García C, Núñez Nagy S. Estudio inicial para el diagnóstico precoz de problemas de desarrollo. TECHNO REVIEW. International Technology, Science and Society Review /Revista Internacional De Tecnología, Ciencia Y Sociedad 2022;11(5):1 View
  11. Kalenjuk E, Wilson S, Subban P, Laletas S. Art‐based research to explore children's lived experiences of dysgraphia. Children & Society 2024;38(2):381 View
  12. Kunhoth J, Al Maadeed S, Saleh M, Akbari Y. CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children. Expert Systems with Applications 2023;231:120740 View
  13. Megumi A, Shin J, Uchida Y, Yasumura A. Increased Activity in the Prefrontal Cortex Related to Planning during a Handwriting Task. Psych 2023;5(3):896 View
  14. Babushkin V, Alsuradi H, Jamil M, Al-Khalil M, Eid M. Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach. Frontiers in Robotics and AI 2023;10 View
  15. Provenzale C, Bonsignori C, Sparaci L, Formica D, Taffoni F. Using Screen-Based Technologies to Assess Handwriting in Children: A Preliminary Study Choosing Human–Machine Interaction. IEEE Access 2023;11:118865 View
  16. Danna J, Puyjarinet F, Jolly C. Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art. Children 2023;10(12):1925 View
  17. Kunhoth J, Al-Maadeed S, Kunhoth S, Akbari Y, Saleh M. Automated systems for diagnosis of dysgraphia in children: a survey and novel framework. International Journal on Document Analysis and Recognition (IJDAR) 2024;27(4):707 View
  18. Kalenjuk E, Subban P, Laletas S, Wilson S. It’s the “ Generic School System That Is Expected on All the kids ”: Dysgraphia Through the Lens of Australian Mothers. International Journal of Disability, Development and Education 2024:1 View
  19. Sharma P, Agarwal B, Yadav G, Jain S. Vision transformer-based model for early detection of dysgraphia among school students. Microsystem Technologies 2024 View

Books/Policy Documents

  1. Mekyska J, Galaz Z, Safarova K, Zvoncak V, Cunek L, Urbanek T, Havigerova J, Bednarova J, Mucha J, Gavenciak M, Smekal Z, Faundez-Zanuy M. Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition. View
  2. Sharma A, Singhal I, Awasthi N, Mehrotra D, Gupta R. Advances in Artificial-Business Analytics and Quantum Machine Learning. View