Generating psychological analysis tables for children's drawings using deep learning

Abstract

The usefulness of drawing-based psychological testing has been demonstrated in a variety of studies. By using the familiar medium of drawing, drawing-based psychological testing can be applied to a wide range of age groups and is particularly effective with children who have difficulty expressing themselves verbally. Drawing tests are usually implemented face-to-face, requiring specialized counseling staff, and can be time-consuming and expensive to apply to large numbers of children. These problems seem to be solved by applying highly developed artificial intelligence techniques. If artificial intelligence (AI) can analyze children's drawings and perform psychological analysis, it will be possible to use it as a service and take tests online or through smartphones. There have been various attempts to automate the drawing of psychological tests by utilizing deep learning technology to process images. Previous studies using classification have been limited in their ability to extract structural information. In this paper, we analyze the House-Tree-Person Test (HTP), one of the drawing psychological tests widely used in clinical practice, by utilizing object detection technology that can extract more diverse information from images. In addition, we extend the existing research that has been limited to the extraction of relatively simple psychological features and generate a psychological analysis table based on the extracted features that can be used to assist experts in the process of psychological testing. Our research findings indicate that the object detection performance achieves a mean Average Precision (mAP) of approximately 92.6∼94.1 %, and the average accuracy of the psychological analysis table is 94.4 %.