How Facial Features Convey Attention in Stationary Environments

Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open-source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified the Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNNs and CRNNs). Interestingly, CRNNs did not appear to perform significantly better than their CNN counterparts. While deep learning methods achieved greater prediction accuracy, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods

How Facial Features and Head Gesture Convey Attention in Stationary Environments

Awareness detection technologies have been gaining traction in a variety of enterprises; Most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in stationary environments such as online classrooms. This study aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support Vector Machine (SVM) we created several prediction models for user attention and identified Histogram of Oriented Gradients (HOGS) to be the greatest predictor of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNN\u27s and CRNN\u27s). Interestingly, CRNN\u27s did not appear to perform significantly better than their CNN counterparts. While deep learning methods definitively performed better, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods.https://digitalscholarship.unlv.edu/durep_podium/1026/thumbnail.jp

Modeling COVID-19 Infection Rates using SIR and ARIMA Models

With the onset of the COVID-19 pandemic, it has become of increasing interest to both monitor and predict the growth of its infection rates. In order to analyze the accuracy of epidemiological prediction, we consider two different models for prediction, the Susceptible Infected and Removed (SIR), and Autoregressive Integrated Moving Average (ARIMA) models. Using a dataset of Clark County COVID-19 infections, we create various ARIMA and SIR models that attempt to predict the progression of COVID-19 infections whilst comparing these predictions to the dataset. We observed that the ARIMA model performed more accurately overall, having a much lower Root Mean Squared Error than its counterpart.https://digitalscholarship.unlv.edu/durep_posters/1005/thumbnail.jp