VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals
<|reference_start|>VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals: This paper presents VisioPhysioENet, a novel multimodal system that leverages visual cues and physiological signals to detect learner engagement. It employs a two-level approach for visual feature extraction using the Dlib library for facial landmark extraction and the OpenCV library for further estimations. This is complemented by extracting physiological signals using the plane-orthogonal-to-skin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various engagement levels. We rigorously evaluate VisioPhysioENet on the DAiSEE dataset, where it achieves an accuracy of 63.09%, demonstrating a superior ability to discern various levels of engagement compared to existing methodologies. The proposed system's code can be accessed at https://github.com/MIntelligence-Group/VisioPhysioENet.<|reference_end|>
@article{singh2024visiophysioenet:,
    title={VisioPhysioENet: Multimodal Engagement Detection using Visual and
  Physiological Signals},
    author={Alakhsimar Singh, Nischay Verma, Kanav Goyal, Amritpal Singh, Puneet
  Kumar and Xiaobai Li},
    journal={arXiv preprint arXiv:2409.16126},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16126},
    primaryClass={cs.CV}
}