@article{petrin_maternal_2024,

title = {Maternal childhood emotional abuse increases cardiovascular responses to children’s emotional facial expressions},

author = {R. Pétrin and A. Bérubé and É. St-Pierre and C. Blais},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192637581&doi=10.1371%2fjournal.pone.0302782&partnerID=40&md5=c464b30fe7cc5b7b0baaf865fdf1f6de},

doi = {10.1371/journal.pone.0302782},

issn = {19326203 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {PLoS ONE},

volume = {19},

number = {5 May},

abstract = {Parents with a history of childhood maltreatment may be more likely to respond inadequately to their child’s emotional cues, such as crying or screaming, due to previous exposure to prolonged stress. While studies have investigated parents’ physiological reactions to their children’s vocal expressions of emotions, less attention has been given to their responses when perceiving children’s facial expressions of emotions. The present study aimed to determine if viewing facial expressions of emotions in children induces cardiovascular changes in mothers (hypo- or hyper-arousal) and whether these differ as a function of childhood maltreatment. A total of 104 mothers took part in this study. Their experiences of childhood maltreatment were measured using the Childhood Trauma Questionnaire (CTQ). Participants’ electrocardiogram signals were recorded during a task in which they viewed a landscape video (baseline) and images of children’s faces expressing different intensities of emotion. Heart rate variability (HRV) was extracted from the recordings as an indicator of parasympathetic reactivity. Participants presented two profiles: one group of mothers had a decreased HRV when presented with images of children’s facial expressions of emotions, while the other group’s HRV increased. However, HRV change was not significantly different between the two groups. The interaction between HRV groups and the severity of maltreatment experienced was marginal. Results suggested that experiences of childhood emotional abuse were more common in mothers whose HRV increased during the task. Therefore, more severe childhood experiences of emotional abuse could be associated with mothers’ cardiovascular hyperreactivity. Maladaptive cardiovascular responses could have a ripple effect, influencing how mothers react to their children’s facial expressions of emotions. That reaction could affect the quality of their interaction with their child. Providing interventions that help parents regulate their physiological and behavioral responses to stress might be helpful, especially if they have experienced childhood maltreatment. © 2024 Public Library of Science. All rights reserved.},

note = {Publisher: Public Library of Science},

keywords = {adult, alcohol consumption, analysis of variance, article, blood pressure, cardiovascular response, Child, Child Abuse, Childhood, Childhood Trauma Questionnaire, demographics, electrocardiogram, Electrocardiography, emotion, Emotional Abuse, Emotions, Ethnicity, Facial Expression, female, Heart Rate, heart rate variability, human, human experiment, Humans, Likert scale, male, mother, mother child relation, Mother-Child Relations, Mothers, parasympathetic tone, physical activity, physiology, post hoc analysis, psychology, questionnaire, sexual abuse, Surveys and Questionnaires},

pubstate = {published},

tppubtype = {article}

}

@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}

}