State-of-the-art CNN Architectures for Assessing Fine Motor Skills: a Comparative Study

Abstract

It is considered that children’s normal growth depends on their ability to use their fine motor skills. Deficits in fine motor skills in preschool children can interfere with even basic daily activities. Research also links these problems to future challenges. Therefore, early identification of preschool children’s fine motoric abilities is considered essential. However, the assessment of the development of fine motor skills is considered to be a rather complex process. Complex and time-consuming methods are used for their reliable assessment, which also requires the presence of educational experts. The aim of this study is to investigate whether it is possible to create a simple and useful tool for assessing fine motor skills in preschool children, based on convolutional neural networks. For this purpose, a comparative study between 5 state-of-the-art CNN architectures is carried out, to investigate their accuracy in assessing fine motor skills. Drawings of Greek students from public kindergartens were used to train the investigated CNN models. The Griffiths II and the Eye Coordination Scale were used to assess the developmental age of preschool children. The findings demonstrate that, although challenging, automatic and precise detection of fine motor skills is feasible if a larger dataset is used to train deep learning models.