Analyzing Children's Fine Motor Skills and Emotions Through Handwriting Features: A Valuable Dataset

Published: 4 December 2023| Version 1 | DOI: 10.17632/jkdxpvcb23.1
Nurul Zainal Fanani, Nurul Zainal Fanani,
, Festa Yumpi Rahmanawati


Handwriting can reveal crucial insights into an individual's motor skills and emotional state. This data article explores handwriting features to assess a child's Fine Motor Skills (FMS) and emotions while writing. The study encompasses two distinct handwriting tasks: sentence bolding and word copying. Three psychologists use These tasks to observe children's expressions and movements, enabling a comprehensive analysis of their emotional responses and FMS proficiency during the writing process. The data acquisition process unfolds in real-time, facilitated by a high-performance digitizer known as WACOM Cintiq 13HD, which operates at a sampling frequency of 220 Hz. The resulting handwriting dataset comprises seven variables and three target data collected from 98 elementary students aged 6 to 9. Due to the scarcity of datasets tailored to children's FMS and emotions within scientific research and education, this dataset emerges as a valuable resource across various fields, including psychology, machine learning, data mining, and more, promising to contribute significantly to empirical research and educational initiatives.



Universitas Muhammadiyah Jember, Politeknik Negeri Jember, Universitas Islam Negeri Sunan Ampel


Psychology, Educational Psychology, Education, Handwriting, Child Psychology