@article{joudeh_prediction_2023,

title = {Prediction of Continuous Emotional Measures through Physiological and Visual Data †},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163943735&doi=10.3390%2fs23125613&partnerID=40&md5=5e970f0d8c5790b85d8d77a9f3f52a2d},

doi = {10.3390/s23125613},

issn = {14248220},

year  = {2023},

date = {2023-01-01},

journal = {Sensors},

volume = {23},

number = {12},

abstract = {The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.},

note = {Publisher: MDPI},

keywords = {Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment},

pubstate = {published},

tppubtype = {article}

}

@article{benbouriche_self-regulation_2016,

title = {Self-regulation and virtual reality in forensic psychiatry: An emphasis on theoretical underpinnings [Applications de la réalité virtuelle en psychiatrie légale : la perspective de l'autorégulation comme cadre théorique]},

author = {M. Benbouriche and P. Renaud and J. -F. Pelletier and P. De Loor},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954287281&doi=10.1016%2fj.encep.2015.12.001&partnerID=40&md5=3ce15162ac13e345f99c3cbdad987cf2},

doi = {10.1016/j.encep.2015.12.001},

issn = {00137006},

year  = {2016},

date = {2016-01-01},

journal = {Encephale},

volume = {42},

number = {6},

pages = {540–546},

abstract = {Introduction Forensic psychiatry is the field whose expertise is the assessment and treatment of offending behaviours, in particular when offenses are related to mental illness. An underlying question for all etiological models concerns the manner in which an individual's behaviours are organized. Specifically, it becomes crucial to understand how certain individuals come to display maladaptive behaviours in a given environment, especially when considering issues such as offenders’ responsibility and their ability to change their behaviours. Virtual reality Thanks to its ability to generate specific environments, associated with a high experimental control on generated simulations, virtual reality is gaining recognition in forensic psychiatry. Virtual reality has generated promising research data and may turn out to be a remarkable clinical tool in the near future. While research has increased, a conceptual work about its theoretical underpinnings is still lacking. However, no important benefit should be expected from the introduction of a new tool (as innovative as virtual reality) without an explicit and heuristic theoretical framework capable of clarifying its benefits in forensic psychiatry. Objectives Our paper introduces self-regulation perspective as the most suitable theoretical framework for virtual reality in forensic psychiatry. It will be argued that virtual reality does not solely help to increase ecological validity. However, it does allow one to grant access to an improved understanding of violent offending behaviours by probing into the underlying mechanisms involved in the self-regulation of behaviours in a dynamical environment. Illustrations are given as well as a discussion regarding perspectives in the use of virtual reality in forensic psychiatry. © 2015 L'Encéphale, Paris},

note = {Publisher: Elsevier Masson SAS},

keywords = {autoregulation, behavior, Computer Graphics, computer interface, Computer Simulation, conceptual framework, Crime, ecological validity, Environment, Expert Testimony, expert witness, Forensic psychiatry, human, human experiment, Humans, Mental Disorders, procedures, psychology, recognition, theoretical model, User-Computer Interface, Violence, virtual reality},

pubstate = {published},

tppubtype = {article}

}