Effects of system- and media-driven immersive capabilities on presence and affective experience

Standen, Bradley; Anderson, John; Sumich, Alexander; Heym, Nadja

doi:10.1007/s10055-021-00579-2

Cited by 3 publications

(2 citation statements)

References 56 publications

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“…4 School of Social Sciences,, Nottingham Trent University, Shakespeare St, NG1 4FQ Nottingham, UK. 5 Nottingham School of Art & Design, Nottingham Trent University, Shakespeare St, NG1 4FQ Nottingham, UK. 6 School of ADBE, Nottingham Trent University, Nottingham Trent University, Shakespeare St, NG1 4FQ Nottingham, UK.…”

Section: Funding Consent For Publicationmentioning

confidence: 99%

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Rahman

Brown

Mahmud

et al. 2022

Preprint

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Virtual-reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required.In this work, we have explored a series of machine learning models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of machine learning models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of Virtual Reality Exposure Therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

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Section: Funding Consent For Publicationmentioning

confidence: 99%

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Rahman

Brown

Mahmud

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Virtual reality exposure therapy (VRET) is one of the most promising novel treatments, enabled by its superior immersive capabilities that generate a greater sense of presence and enhance user effects, especially for negatively valenced, high arousal stimuli [ 5 ]. Over the last two decades VRET, encompassing psychological treatment principles and enabled by advancing display and computing technology developments, has become a popular digital intervention for various psychological disorders [ 6 , 7 ], being as effective as in vivo (i.e., face-to-face) exposure therapy post-intervention [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

et al. 2023

Self Cite

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Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

show abstract

Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

et al. 2022

View full text Add to dashboard Cite

Effects of system- and media-driven immersive capabilities on presence and affective experience

Cited by 3 publications

References 56 publications

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

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