Affective Virtual Reality System (AVRS): Design and Ratings of Affective VR Scenes

Shu, Lin; Xu, Xiangmin; Liao, Dan

doi:10.1109/icvrv.2017.00072

Cited by 22 publications

(10 citation statements)

References 5 publications

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“…Zhang et al proposed a novel emotion evocation system called Affective Virtual Reality System (AVRS, Figure 4 b, [ 17 ]), which was composed of eight emotive VR scenes ( Figure 4 c) and their three-dimensional emotion indexes that were evaluated by 100 subjects using the Self-Assessment Manikin (SAM). Color, sound and other features were extracted to create affective VR scenes.…”

Section: Emotion Modelsmentioning

confidence: 99%

A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

Yang

et al. 2018

Sensors

Self Cite

630

352

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Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

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Section: Emotion Modelsmentioning

confidence: 99%

A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

Yang

et al. 2018

Sensors

Self Cite

630

352

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show abstract

“…Looking into VR focused stimuli, the researchers designed their own stimuli database that would fit into their VR environment since there is a lack of available datasets as those currently available datasets were designed for viewing from a monitor's perspectives. Affective Virtual Reality System (AVRS) is a new database designed by Zhang et al [ 114 ] which combines IAPS [ 128 ], IADS, and China Affective Video System (CAVS) to produce a virtual environment that would accommodate VR headset for emotion classification. However, the dataset has only been evaluated using Self-Assessment Manikin (SAM) to evaluate the effectiveness of the AVRS system delivery of emotion and currently is still not made available for public access.…”

Section: Examining Previous Studiesmentioning

confidence: 99%

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Suhaimi

Mountstephens

Teo

2020

Computational Intelligence and Neuroscience

228

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Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

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“…Emotional scenes are often used to manipulate the valence of people (Shumailov and Gunes, 2017;Mavridou et al, 2018b;Zheng et al, 2020). Such scenes can be taken from a database (Samson et al, 2016) or be tested in a pre-study to see what emotions they trigger (Zhang W. et al, 2017).…”

Section: Arousal and Valencementioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

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Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

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Affective Virtual Reality System (AVRS): Design and Ratings of Affective VR Scenes

Cited by 22 publications

References 5 publications

A Review of Emotion Recognition Using Physiological Signals

A Review of Emotion Recognition Using Physiological Signals

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

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