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

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.