Engagement Detection

Engagement detection aims to automatically assess the level of user involvement in various interactive settings, such as e-learning, healthcare, and mental health interventions. Current research focuses on multimodal approaches, combining visual (facial expressions, body posture), physiological (cardiovascular activity), and auditory (speech) data, often employing machine learning classifiers like support vector machines and deep neural networks (e.g., DenseNet, ResNet) for analysis. These advancements are improving the accuracy of engagement assessment across diverse contexts, leading to more personalized and effective interventions in education and healthcare. The development of high-quality, multi-modal datasets is also a key area of focus, enabling more robust and generalizable models.