VisioPhysioENet: Visual Physiological Engagement Detection Network
This work addresses engagement detection for learners, but it is incremental as it builds on existing multimodal approaches with specific feature extraction methods.
The paper tackled the problem of detecting learner engagement by developing VisioPhysioENet, a multimodal system that integrates visual and physiological signals, achieving an accuracy of 63.09% on the DAiSEE dataset and outperforming a comparable model by 8.6%.
This paper presents VisioPhysioENet, a novel multimodal system that leverages visual and physiological signals to detect learner engagement. It employs a two-level approach for extracting both visual and physiological features. For visual feature extraction, Dlib is used to detect facial landmarks, while OpenCV provides additional estimations. The face recognition library, built on Dlib, is used to identify the facial region of interest specifically for physiological signal extraction. Physiological signals are then extracted using the plane-orthogonal-toskin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various levels of engagement. We thoroughly tested VisioPhysioENet on the DAiSEE dataset. It achieved an accuracy of 63.09%. This shows it can better identify different levels of engagement compared to many existing methods. It performed 8.6% better than the only other model that uses both physiological and visual features.