Using Screen-Based Technologies to Assess Handwriting in Children: A Preliminary Study Choosing Human–Machine Interaction

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

Evaluating Handwriting Skills through Human-Machine Interaction : A New Digitalized System for Parameters Extraction

Handwriting is an important component of academic curricula and grapho-motor skills (GMS) support learning, reading, memory and self-confidence. Teachers and clinicians report increase in children experiencing problems with acquiring fluid and legible handwriting. To date gold-standard tests evaluating children's GMS, mostly rely on pen and paper tests, requiring extensive coding time and subject to high inter-rater variability. This work presents preliminary data on a new digital platform for Grapho-motor Handwriting Evaluation & Exercise (GHEE), attempting to overcome limitations of available digitalized methods for GMS evalution. In fact, contrary to previous systems, GHEE design originated from comparisons among multiple standardized tests and was based on a human-machine interaction approach. GHEE hardware and software is presented as well as data on preliminary testing. Cursive handwriting data from six adult volunteers was analyzed according to six parameters of relevance, both automatically (i.e., using GHEE software) and manually (i.e., by a human coder). Comparisons among machine and human data sets allowed parsing out parameters to be extracted automatically and parameters requiring human-machine interaction. Results confirmed platform efficacy and feasibility of the proposed approach

Right hand motor control difference between adults with and without autism

Recent studies have provided growing evidence of motor differences in individuals with autism (e.g., Bhat, 2023; Miller et al, 2024). Also, atypical brain lateralisation in autism has been extensively documented (e.g., Floris et al., 2021; Li et al., 2023). However, the precise relationship between this atypical brain lateralisation and motor control challenges in autism remains not fully understood