A Survey on Affective and Cognitive VR

Luong, Tiffany; Lécuyer, Anatole; Martin, Nicolas; Argelaguet, Ferran

doi:10.1109/tvcg.2021.3110459

Cited by 11 publications

(9 citation statements)

References 130 publications

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“…Unlike self-reports that depend on the subject's selfawareness and accuracy of responding, observational methods typically evaluate facial and body behaviours [107]. Nonverbal behaviours are associated with proxemics, gait, body posture and facial expressions, and can highlight patterns and frequency of behaviours associated [107]. Some observational methods identified in the literature include head tracking, proxemic measurements, posturographic measurements of the skeleton and gait.…”

Section: Observational Methodsmentioning

confidence: 99%

“…Various methods for measuring affective states and their behavioural responses have been proposed in the last decades. These methods can be broken down into the following categories, as substantiated by Kaplan et al and Luong et al [106], [107]: self-reports, physiological measurements, and observational methods.…”

Section: The Measurement Of Affective States and Behavioural Response...mentioning

confidence: 99%

“…Self-report measures are subjective methods that require individuals to describe their affective states and behavioural responses, either in the present or a previous moment [107]. Examples from the literature include Statetrait anxiety inventory, the Buss and Perry Aggression Questionnaire, Traumatic Life Events Questionnaire, IRI and Likert scales to measure affective states from a discrete perspective [107]. These questionnaires measure affective states and their propensity for behavioural responses (ie.…”

Section: Self-report Measuresmentioning

confidence: 99%

See 2 more Smart Citations

Threat Perception Captured by Emotion, Motor and Empathetic System Responses: A Systematic Review

Jacobs,

Deligianni,

Pollick

2024

IEEE Trans. Affective Comput.

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Section: Observational Methodsmentioning

confidence: 99%

Section: The Measurement Of Affective States and Behavioural Response...mentioning

confidence: 99%

Section: Self-report Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Threat Perception Captured by Emotion, Motor and Empathetic System Responses: A Systematic Review

Jacobs,

Deligianni,

Pollick

2024

IEEE Trans. Affective Comput.

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“…The EmojiGrid, for example, has been proposed as a self-report tool for the assessment of VRevoked emotions [116]. The interested reader is referred to [117][118][119] for more details on user emotional/affective state assessment in VR.…”

Section: Questionnaires For Affective/mental State Monitoringmentioning

confidence: 99%

Immersive media experience: a survey of existing methods and tools for human influential factors assessment

2022

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Virtual reality (VR) applications, especially those where the user is untethered to a computer, are becoming more prevalent as new hardware is developed, computational power and artificial intelligence algorithms are available, and wireless communication networks are becoming more reliable, fast, and providing higher reliability. In fact, recent projections show that by 2022 the number of VR users will double, suggesting the sector was not negatively affected by the worldwide COVID-19 pandemic. The success of any immersive communication system is heavily dependent on the user experience it delivers, thus now more than ever has it become crucial to develop reliable models of immersive media experience (IMEx). In this paper, we survey the literature for existing methods and tools to assess human influential factors (HIFs) related to IMEx. In particular, subjective, behavioural, and psycho-physiological methods are covered. We describe tools available to monitor these HIFs, including the user's sense of presence and immersion, cybersickness, and mental/affective states, as well as their role in overall experience. Special focus is placed on psycho-physiological methods, as it was found that such in-depth evaluation was lacking from the existing literature. We conclude by touching on emerging applications involving multiple-sensorial immersive media and provide suggestions for future research directions to fill existing gaps. It is hoped that this survey will be useful for researchers interested in building new immersive (adaptive) applications that maximize user experience.

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“…VR technologies immerse users in a simulated environment that, for the duration of the experience, creates an alternative reality. The realistic authenticity and highly controllable nature of VR makes it an ideal candidate for affect based biofeedback training [4].…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Multi-level Stress Assessment From Electrocardiogram Data in a Virtual Reality Environment Using Contrastive Self Supervision

Rabbani

2024

Preprint

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<p>Electrocardiogram (ECG) data is an ideal option to assess stress in Virtual Reality (VR) applications due to its non-invasive nature and high correlation to changes in stress levels. Yet, most studies on stress assessment only collect binary measurements. Developing a multi-level assessment is necessary for a biofeedback-based application. Existing studies annotate and classify a single experience (e.g. watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. Furthermore, existing works heavily rely on supervision information to train stress assessment models. However, obtaining supervised labels for training data is an expensive and labor intensive task that is not scalable on large datasets. Given the advancements in data driven technologies, supervised learning prevents us from leveraging unlabeled data excessively available nowadays. In this thesis, we report our findings on a new study on VR multi-level stress assessment. ECG data was collected from 12 participants experiencing a VR roller coaster. The VR experience was then manually labeled in 10-second segments to three stress levels. We then propose a contrastive self-supervised learning approach for stress assessment that is superior to non-contrastive self-supervised approaches. Experimental results show that when compared to non-contrastive self-supervised learning, we obtained a 9% increase in accuracy on the WESAD dataset and a 3.7% increase on our collected data at the Ryerson Multimedia Lab.</p>

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A Survey on Affective and Cognitive VR

Cited by 11 publications

References 130 publications

Threat Perception Captured by Emotion, Motor and Empathetic System Responses: A Systematic Review

Threat Perception Captured by Emotion, Motor and Empathetic System Responses: A Systematic Review

Immersive media experience: a survey of existing methods and tools for human influential factors assessment

Multi-level Stress Assessment From Electrocardiogram Data in a Virtual Reality Environment Using Contrastive Self Supervision

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