Assessment of Children's Postural Control Ability Using Graph Convolutional Networks

Abstract

In occupational therapy, there is a need to assess children's posture control abilities for screening of sensory integration disorder. However, it is difficult to quantitatively evaluate children's postural control abilities based on occupational therapists' subjective assessments. Previous studies have attempted to address the issue using human pose estimation methods, but they only used a limited number of keypoints, such as the knees and elbows. In this study, we propose a model to predict occupational therapists' subjective assessments. This model is created based on spatial temporal graph convolutional network and leverages all the keypoints obtained through a human pose estimation method. The experimental results showed that the Spearman's correlation coefficient with occupational therapists' evaluation was 0.848. Furthermore, the findings suggested that the lower body is more important than the upper body. In addition to the previously considered knee and ankle, the relationship between the heel and toes is also crucial. The achievement could help identify keypoint features that have previously been overlooked but are essential, contributing to the development of more effective assessments in occupational therapy.