Assessment of Visual Motor Integration via Hand-Drawn Imitation: A Pilot Study

Author:

Zhang Dinghuang1ORCID,Lu Baoli2ORCID,Guo Jing3,He Yu4,Liu Honghai1

Affiliation:

1. School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK

2. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China

3. International College, University of Creativity Art, Farnham GU9 7DS, UK

4. Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China

Abstract

Copious evidence shows that impaired visual–motor integration (VMI) is intrinsically linked to the core deficits of autism spectrum disorder (ASD) and associated with an anomalous social capability. Therefore, an effective evaluation method of visual–motor behaviour can provide meaningful insight into the evaluation of VMI towards social capability. The current pilot study aims to explore the appropriate quantified metrics for evaluating VMI ability based on a hand-drawn imitation protocol. First, a simple and interesting hand-drawn protocol was designed, and six healthy participants were recruited to perform the task. Then, based on the collected hand–eye behaviour data, several metrics were applied to infer the participant’s social capability and VMI in engagement and visual–motor complexity based on hand–eye properties with Hausdorff distance and cross-recurrence quantification analysis (CRQA). Finally, those quantified metrics were verified through statistical significance. This study proposed a set of quantitative metrics to construct a comprehensive VMI evaluation, including outcome and progress measures. The results revealed the proposed method as a directly interpretable indicator providing a promising computational framework and biomarker for VMI evaluation, paving the way for its future use in ASD diagnosis and guiding intervention.

Funder

China Scholarship Council

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. THE THE WORD CHAIN TEST: THE NORMATIVE DATA FOR STUDENTS FROM GRADES 1 TO 9;SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference;2024-05-22

2. Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification;PLOS ONE;2024-04-22

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