Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:5128-5131. doi: 10.1109/EMBC48229.2022.9871538.
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.
书写是学术课程的一个重要组成部分，也是图运动技能（GMS）支持学习、阅读、记忆和自信的关键。教师和临床医生报告说，越来越多的孩子在获得流畅、清晰的书写方面遇到问题。迄今为止，评估儿童 GMS 的黄金标准测试主要依赖于纸笔测试，需要大量的编码时间，并且受到评分者间变异性的影响。本工作介绍了一种新的数字平台 Grapho-motor Handwriting Evaluation & Exercise（GHEE）的初步数据，该平台旨在克服现有 GMS 评估数字化方法的局限性。事实上，与以前的系统不同，GHEE 的设计源于对多个标准化测试的比较，并基于人机交互方法。展示了 GHEE 的硬件和软件以及初步测试的数据。根据六个相关参数，对六名成年志愿者的手写体数据进行了分析，包括自动（即使用 GHEE 软件）和手动（即由人工编码员）两种方式。机器数据和人工数据的比较允许提取可以自动提取的参数和需要人机交互的参数。结果证实了该平台的有效性和所提出方法的可行性。
