Framework Procedural para Geração de Dados Sintéticos Baseados em Simulação Estatística: Uma Abordagem para Análise de Comportamento Humano - Gonçalves, Jáder Louis de S.; Amorim, Wyllgner F.; Sales, Nicolas F. C.; Cunha, Lucas Marques da (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the Web, 2025)
Data sets for training convolutional neural networks (CNNs) pose a challenge due to the difficulty of obtaining them and the sensitive nature of the information involved, leading to high costs, scarcity, and potential biases. To address these limitations, synthetic data generation provides a viable solution, enabling controlled and ethical research in human behavior. The main objective was to develop a framework capable of simulating environments and generating data procedurally for AI training, focusing on engagement monitoring and the identification of behavioral deviations in educational and corporate contexts. Among the specific objectives, the creation of an application persona, Visage Track, stands out, designed to calculate engagement and behavioral deviations, thereby validating the developed techniques. The proposed approach integrates procedural generation methods into a complex statistical simulation, allowing for customizable and controlled scenarios without the need for direct interaction with real subjects. The framework and Visage Track were constructed to facilitate experiments in simulated environments. In the evaluation, the prototype demonstrated high accuracy in presence detection (up to 98%), consistent behavioral simulations across multiple scenarios, and the ability to generate scalable, multimodal datasets. These results indicate the framework’s potential to support research in multimedia content generation, multimodal interaction, and affective computing, offering an adaptable and ethical infrastructure for studies involving human behavior in controlled environments.