Wang Chenyang, Tozadore Daniel Carnieto, Bruno Barbara, Dillenbourg Pierre
CHILI Lab, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, 1015, Switzerland.
SARAI Lab, Karlsruhe Institute of Technology, Karlsruhe, 76131, Germany.
Sci Rep. 2025 Jul 2;15(1):23099. doi: 10.1038/s41598-025-08405-4.
Various technological applications for body posture correction have been proposed in order to improve handwriting or facilitate its learning for children, under the assumption that a better posture promotes better handwriting. However, very little research investigates the correlation between body posture quality and handwriting quality. Moreover, investigating this correlation typically necessitates the expertise of human observers, leading to high costs, slow progress, and potential subjectivity issues. Consequently, this method may not be suitable for educational environments that require prompt feedback and interventions. In this paper, we present a fully-automated pipeline for the real-time assessment of body posture quality, which builds upon validated scales from ergonomics, which relies on red green blue depth (RGB-D) camera data to compute the rapid entire body assessment (REBA)/rapid upper limb assessment (RULA) body posture scores. Together with a state-of-the-art tool for the automated, real-time assessment of handwriting quality, we applied our pipeline in an experiment at school involving 31 children, to quantitatively and objectively investigate (i) the correlation between body posture quality scores and handwriting quality measures, as well as (ii) the impact that interventions aimed at improving the children's body posture have on their handwriting quality. Our findings (i) demonstrate the correlations between specific postural element quality assessment scores (e.g., neck score) and handwriting dimensions (e.g., static features), and (ii) indicate that interventions aiming to improve body posture quality also have an immediate, significant positive effect on handwriting quality.
为了改善儿童的书写或促进其书写学习，人们提出了各种用于身体姿势矫正的技术应用，其假设是更好的姿势能促进更好的书写。然而，很少有研究调查身体姿势质量与书写质量之间的相关性。此外，研究这种相关性通常需要人类观察者的专业知识，这导致成本高昂、进展缓慢以及潜在的主观性问题。因此，这种方法可能不适用于需要及时反馈和干预的教育环境。在本文中，我们提出了一种用于实时评估身体姿势质量的全自动流程，该流程基于人体工程学的有效量表构建，依靠红绿蓝深度（RGB-D）相机数据来计算快速全身评估（REBA）/快速上肢评估（RULA）身体姿势分数。连同一种用于自动实时评估书写质量的先进工具，我们在一所学校对31名儿童进行的实验中应用了我们的流程，以定量和客观地研究（i）身体姿势质量分数与书写质量指标之间的相关性，以及（ii）旨在改善儿童身体姿势的干预措施对其书写质量的影响。我们的研究结果（i）证明了特定姿势元素质量评估分数（如颈部分数）与书写维度（如静态特征）之间的相关性，并且（ii）表明旨在改善身体姿势质量的干预措施对书写质量也有直接、显著的积极影响。
