VisioPhysioENet: Visual Physiological Engagement Detection Network

TL;DR

VisioPhysioENet is a multimodal system that combines visual and physiological data to accurately detect learner engagement, outperforming existing methods by integrating advanced feature extraction and machine learning techniques.

Contribution

It introduces a novel multimodal approach using visual and physiological signals with a two-level feature extraction method for engagement detection.

Findings

Achieved 63.09% accuracy on the DAiSEE dataset.

Performed 8.6% better than existing models using both physiological and visual features.

Effectively integrates facial landmark detection and physiological signals for engagement assessment.

Abstract

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.