Author
Listed:
- Simone Toffoli
- Linda Greta Dui
- Stefania Fontolan
- Francesca Lunardini
- Milad Malavolti
- Chiara Piazzalunga
- Alice Donati
- Cesare Cornoldi
- Cristiano Termine
- Simona Ferrante
Abstract
The assessment of handwriting is fundamental for identifying difficulties, which may have long-term negative consequences. However, standard evaluation typically focuses only on the final handwritten product. For this reason, Italian guidelines recommended supporting traditional evaluation with digital tools to also analyze the handwriting process. A sensorized ink pen used on paper was employed by over 700 students, ranging from first grade in Italian primary school to third grade in lower secondary school, to execute two tasks of the BVSCO-3, the gold standard for handwriting assessment. From sensorized ink pen data, handwriting indicators in the domains of Time, Force, Smoothness, Tilt, and Frequency were extracted. These indicators were then analyzed to examine their correlation with clinical scores, to model cross-sectional trends across grades, and to identify handwriting difficulties. The correlation analysis revealed significant relationships between the indicators and the clinical score, particularly for the Time domain. A cross-sectional statistical analysis showed that the indicators follow developmental trends compatible with handwriting learning curves reported in the literature: for many indicators, a performance plateau was reached in grade 3, from both motor and processing perspectives. Lastly, binary classification models successfully distinguished subjects with handwriting difficulties (based on BVSCO-3 results) from proficient writers. The sensorized ink pen allowed uncovering relevant characteristics of children’s handwriting process, while guaranteeing ecological data acquisition conditions. Its use could pave the way for a prompt identification of handwriting difficulties in school settings, thus facilitating an efficient referral to clinical services.Author summary: Learning to write is an important part of a child’s development, and persistent handwriting difficulties can affect school performance and self-confidence. Traditionally, handwriting is assessed by looking only at the final written text, which may miss important information about how the writing is produced. In this study, we explored whether a sensorized pen that writes on normal paper can provide useful additional insights into children’s handwriting. We worked with over seven hundred Italian primary and lower secondary school students from all grades, who completed standard handwriting tasks while using the sensorized pen. This tool allowed us to measure aspects of the writing process, such as how long writing takes, how much force is applied, how smooth the movements are, and how the pen is held. We found clear links between these measurements and clinical handwriting scores. Importantly, the digital indicators reflected well-known patterns of handwriting development, showing that many skills stabilize around grade 3. Our results also show that data from the digital pen can distinguish children with handwriting difficulties from proficient writers. We believe this approach offers an easy-to-use and natural way to support the identification of handwriting problems in schools, potentially improving access to clinical assessment and intervention.
Suggested Citation
Simone Toffoli & Linda Greta Dui & Stefania Fontolan & Francesca Lunardini & Milad Malavolti & Chiara Piazzalunga & Alice Donati & Cesare Cornoldi & Cristiano Termine & Simona Ferrante, 2026.
"Quantitative analysis of handwriting kinematics in primary and lower secondary school children through a sensorized ink pen: A cross-sectional population-based study,"
PLOS Digital Health, Public Library of Science, vol. 5(7), pages 1-26, July.
Handle:
RePEc:plo:pdig00:0001503
DOI: 10.1371/journal.pdig.0001503
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pdig00:0001503. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: digitalhealth (email available below). General contact details of provider: https://journals.plos.org/digitalhealth .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.