Unveiling the Potential of ChatGPT and YOLOv7 for Evaluating Children's Emotions Using Their Artistic Expressions

Recent advancements in large language models (LLMs) have sparked considerable interest in their potential applications across various healthcare domains. One promising prospect is leveraging these generative models to accurately predict children's emotions by combining computer vision and natural language processing techniques. However, understanding children's emotional states based on their artistic expressions is equally crucial. To address this challenge, this paper presents a pipelined architecture comprising YOLOv7 and the powerful GPT-3.5 Turbo language model, where YOLOv7 is employed for object detection using art therapy imaging annotations, while GPT-3.5 interprets the sketches. After rigorously evaluating the proposed framework through a series of comprehensive experiments, we observed that our model achieved high confidence scores for both object detection and emotion interpretation. The robust performance of the proposed framework not only aids in explaining children's art but also provides valuable insights for parents and therapists. This capability enables them to better understand children's emotional states based on their artistic expressions, ultimately facilitating improved support and care.