Intelligent Behaviour Recognition in Developmental Disorder Screening of Adolescents Using Augmented Deep CNN Models
Xinlong Zhang, Zhen Bin It, Jovan Bowen Heng, Tee Hui Teo · Preprints.org · 2025
Adolescent activity detection plays a critical role in ensuring safety, supporting early diagnosis of developmental disorders, and enhancing educational environments. This paper presents a deep learning-based approach for recognizing and classifying adolescent activities using the ResNet-18 convolutional neural network (CNN). The proposed system is trained and evaluated on a comprehensive dataset of adolescents’ activities, incorporating data augmentation techniques to improve generalization. Experimental results demonstrate that the ResNet-18 model achieves high accuracy in detecting various adolescent behaviours outperforming traditional CNN models. The lightweight nature of ResNet-18 ensures real-time performance, making it suitable for practical applications in smart surveillance, healthcare monitoring, and educational tools. Future work will focus on integrating more diverse datasets and optimizing the model for deployment in real-world environments. This study highlights the potential of ResNet-18 as an effective solution for intelligent adolescent activity recognition systems.