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Full-Text Articles in Psychology
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray
Electrical & Computer Engineering Theses & Dissertations
Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …
Toward Culturally Relevant Emotion Detection Using Physiological Signals, Khadija Zanna
Toward Culturally Relevant Emotion Detection Using Physiological Signals, Khadija Zanna
USF Tampa Graduate Theses and Dissertations
Research shows that emotional distress has a statistically significant impact on a student’s grade point average and intent to drop out of college. Because students of different races have varying college experiences, it is important to understand the emotional experiences of different racial groups to better support students’ needs and academic success. In this work, we explore several physiological responses to ten different emotional stimuli captured from 140 students. We employ unsupervised learning via the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and supervised learning via Random Forests and Support Vector machines to analyze clustering partitions and classification …
Feature Space Augmentation: Improving Prediction Accuracy Of Classical Problems In Cognitive Science And Computer Vison, Piyush Saxena
Feature Space Augmentation: Improving Prediction Accuracy Of Classical Problems In Cognitive Science And Computer Vison, Piyush Saxena
Dissertations (1934 -)
The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as …
Computational Modeling Of Facial Response For Detecting Differential Traits In Autism Spectrum Disorders, Manar D. Samad
Computational Modeling Of Facial Response For Detecting Differential Traits In Autism Spectrum Disorders, Manar D. Samad
Electrical & Computer Engineering Theses & Dissertations
This dissertation proposes novel computational modeling and computer vision methods for the analysis and discovery of differential traits in subjects with Autism Spectrum Disorders (ASD) using video and three-dimensional (3D) images of face and facial expressions. ASD is a neurodevelopmental disorder that impairs an individual’s nonverbal communication skills. This work studies ASD from the pathophysiology of facial expressions which may manifest atypical responses in the face. State-of-the-art psychophysical studies mostly employ na¨ıve human raters to visually score atypical facial responses of individuals with ASD, which may be subjective, tedious, and error prone. A few quantitative studies use intrusive sensors on …