@article{joudeh_predicting_2024,

title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},

author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},

doi = {10.3390/s24134398},

issn = {14248220 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {13},

abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},

pubstate = {published},

tppubtype = {article}

}

@article{robillard_anxiety_2003,

title = {Anxiety and Presence during VR Immersion: A Comparative Study of the Reactions of Phobic and Non-phobic Participants in Therapeutic Virtual Environments Derived from Computer Games},

author = {G. Robillard and S. Bouchard and T. Fournier and P. Renaud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0142063106&doi=10.1089%2f109493103769710497&partnerID=40&md5=0d245828ebefb17548822c4c316f5721},

doi = {10.1089/109493103769710497},

issn = {10949313 (ISSN)},

year  = {2003},

date = {2003-01-01},

journal = {Cyberpsychology and Behavior},

volume = {6},

number = {5},

pages = {467–476},

abstract = {Virtual reality can be used to provide phobic clients with therapeutic exposure to phobogenic stimuli. However, purpose-built therapeutic VR hardware and software can be expensive and difficult to adapt to individual client needs. In this study, inexpensive and readily adaptable PC computer games were used to provide exposure therapy to 13 phobic participants and 13 non-phobic control participants. It was found that anxiety could be induced in phobic participants by exposing them to phobogenic stimuli in therapeutic virtual environments derived from computer games (TVEDG). Assessments were made of the impact of simulator sickness and of sense of presence on the phobogenic effectiveness of TVEDGs. Participants reported low levels of simulator sickness, and the results indicate that simulator sickness had no significant impact on either anxiety or sense of presence. Group differences, correlations, and regression analyses indicate a synergistic relationship between presence and anxiety. These results do not support Slater's contention that presence and emotion are orthogonal.},

keywords = {Adolescent, adult, Anxiety, article, clinical article, computer, computer program, Computer Simulation, Computer-Assisted, controlled study, correlation analysis, Desensitization, emotion, exposure, female, game, human, Humans, male, Matched-Pair Analysis, Middle Aged, Neuropsychological Tests, phobia, Phobic Disorders, Psychologic, psychotherapy, Reality Testing, Reference Values, regression analysis, Self Concept, Space Perception, symptom, Therapy, User-Computer Interface, Video Games, virtual reality, visual stimulation},

pubstate = {published},

tppubtype = {article}

}