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  • Silvia Buscaglione

Using screen-based technologies to assess handwriting in children: a preliminary study choosing human-machine interaction

Provenzale, C., Bonsignori, C., Sparaci, L., Formica, D., & Taffoni, F. (2023)

IEEE Access


Abstract:

The acquisition of a fluid and legible handwriting in elementary school has a positive impact on multiple skills (e.g., reading, memory, and learning of novel information). In recent years, the growing percentages of children that encounter mild to severe difficulties in the acquisition of grapho-motor parameters (GMPs) has highlighted the importance of timely and reliable assessments. Unfortunately, currently available tests relying on pen and paper and human-based coding (HBC) require extensive coding time, and provide little or no information on motor processes enacted during handwriting. To overcome these limitations, this work presents a novel screen-based platform for Grapho-motor Handwriting Evaluation & Exercise (GHEE). It was designed to support both fully automatic machine-based coding (MBC) of quantitative GMPs and human-machine interaction coding (MBC+HBC) of GMPs accounting for qualitative aspects of a child’s personal handwriting style (i.e., qualitative GMPs). Our main goal was to test: the GHEE coding approach in a relevant environment to assess its reliability compared to HBC; the efficacy of human-machine interaction in supporting coding of qualitative GMPs; and the possibility to provide data on kinematic aspects of handwriting. The preliminary results on 10 elementary school children showed reliability of fully automatic MBC of quantitative GMPs with respect to traditional HBC, a higher resolution of mixed human-machine interaction systems in assessing qualitative GMPs, and suitability of this technology in providing new information on handwriting kinematics.




2023_Provenzale_Using_Screen-Based_Technologies_to_Assess_Handwriting_in_Children_A_Prelim
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