The ability to measure students’ engagement in an educational setting may facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this paper, a real-time automatic student engagement measure is proposed through investigating two of the main components of engagement: the behavioral engagement and the emotional engagement. A biometric sensor network (BSN) consisting of web cameras, a wall-mounted camera and a high-performance computing machine was designed to capture students’ head poses, eye gaze, body movements, and facial emotions. These low-level features are used to train an AI-based model to estimate the behavioral and emotional engagement in the class environment. A set of experiments was conducted to compare the proposed technology with the state-of-the-art frameworks. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. In addition, it offers superior flexibility to work in any educational environment. Further, this approach allows a quantitative comparison of teaching methods.

Student behavior and lecturer oversight in the classroom is known to modulate study behaviors and impact performance and learning outcomes, but cannot at present be managed for distance learning students. Quantifying and automatically measuring student engagement during lectures in a scalable and accessible manner for these students is essential for improving academic success, but has not been studied widely in natural distance learning environments. We collect video recordings from a screen-mounted camera of students studying online lectures in a mostly unstructured setting and gather annotations from a panel of humans for assessing student engagement levels. We present results on the prediction of different representations of engagement, both with subject-independent and individual-specific models, and quantify the performance gap between the generalized and personalized models for engagement prediction. While the subject-independent performance is challenged by data sparsity, results show that the individual-specific models can predict engagement well even with very few labeled examples.

Osteoporosis is a bone disease characterized by a reduction in bone mass, resulting in an increased risk of fractures. To diagnose the osteoporosis accurately, bone mineral density (BMD) measurements and fracture analysis (FA) of the Vertebral bodies (VBs) are required. In this paper, we propose a robust and 3D shape based method to segment VBs in clinical computed tomography (CT) images in order to make BMD measurements and FA accurately. In this experiment, image appearance and shape information of VBs are used. In the training step, 3D shape information is obtained from a set of data sets. Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region. In the segmentation step, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear combination of Gaussians (LCG), Markov Gibbs Random Field (MGRF), and distance probabilistic model obtained from 3D shape prior is used.

This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.