A framework for physiological indicators of flow in VR games: construction and preliminary evaluation

Bian, Yulong; Yang, Chenglei; Gao, Fengqiang; Li, Huiyu; Zhou, Shi; Li, Hanchao; Sun, Xiaowen; Meng, Xiangxu

doi:10.1007/s00779-016-0953-5

Cited by 97 publications

(72 citation statements)

References 49 publications

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“…Taking advantage of the IVE’s potentialities, in recent years some studies have used IVE and physiological responses, such as EEG, HRV and EDA, in different fields. Phobias 44 – 47 , disorders 48 , driving and orientation 49 , 50 , videogames 51 , quality of experience 52 , presence 53 and visualization technologies 54 , are some examples of these applications. Particularly in emotion research, arousal and relaxation have been analysed in outdoor 55 , 56 and indoor 57 IVEs using EDA.…”

Section: Introductionmentioning

confidence: 99%

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

Marín‐Morales

Greco

Guixeres

et al. 2018

Sci Rep

316

170

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Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with non-immersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject’s arousal and valence perception. The model’s accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.

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Section: Introductionmentioning

confidence: 99%

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

Marín‐Morales

Greco

Guixeres

et al. 2018

Sci Rep

316

170

View full text Add to dashboard Cite

show abstract

“…However, most of the studies that used HRV used very few features from the time domain, such as HR [ 115 , 120 ]. Very few studies used features from the frequency domain, such as HF, LF or HF/LF [ 119 , 126 ] and 2 used non-linear features, such as entropy and Poincare [ 65 , 105 ]. Of the studies that used EDA, the majority used total skin conductance (SC) [ 116 ], but some used tonic (SCL) [ 54 ] or phasic activity (SCR) [ 124 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, until 2016, the majority of the papers featured only HR and SC ( Table 2 ), sometimes in combination with EMG and RSP. From 2016, the research started to include HRV frequency domain and non-linear domain analyses [ 105 , 119 ], and EDA analyses, such as CDA, dividing the signals into tonic and phasic components [ 64 ]. In terms of the CNS, EEG research has been undertaken since 2016, including ERP [ 138 ], power spectral density [ 140 ] and functional connectivity analysis [ 65 ].…”

Section: Discussionmentioning

confidence: 99%

Emotion Recognition in Immersive Virtual Reality: From Statistics to Affective Computing

Marín‐Morales

Llinares

Guixeres

et al. 2020

Sensors

168

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Emotions play a critical role in our daily lives, so the understanding and recognition of emotional responses is crucial for human research. Affective computing research has mostly used non-immersive two-dimensional (2D) images or videos to elicit emotional states. However, immersive virtual reality, which allows researchers to simulate environments in controlled laboratory conditions with high levels of sense of presence and interactivity, is becoming more popular in emotion research. Moreover, its synergy with implicit measurements and machine-learning techniques has the potential to impact transversely in many research areas, opening new opportunities for the scientific community. This paper presents a systematic review of the emotion recognition research undertaken with physiological and behavioural measures using head-mounted displays as elicitation devices. The results highlight the evolution of the field, give a clear perspective using aggregated analysis, reveal the current open issues and provide guidelines for future research.

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“…For example, concentration, loss of time perception, a balance between the player’s skills and the game’s demands, and loss of self-awareness are some of the mutual properties that both flow and immersion exhibit (e.g., Brown and Cairns, 2004 ). Immersive experiences during video game playing are still predominantly measured with questionnaires ( Procci and Bowers, 2011 ; Lee et al, 2014 ; Bian et al, 2016 ). These tools often present variability on the definitional premise upon which they were structured, and thus raise ambiguity over the validity of what they claim to be capturing.…”

Section: Introductionmentioning

confidence: 99%

Flow and Immersion in Video Games: The Aftermath of a Conceptual Challenge

2018

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One of the most pleasurable aspects of video games is their ability to induce immersive experiences. However, there appears to be a tentative conceptualization of what an immersive experience is. In this short review, we specifically focus on the terms of flow and immersion, as they are the most widely used and applied definitions in the video game literature, whilst their differences remain disputable. We critically review the concepts separately and proceed with a comparison on their proposed differences. We conclude that immersion and flow do not substantially differ in current studies and that more evidence is needed to justify their separation.

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A framework for physiological indicators of flow in VR games: construction and preliminary evaluation

Cited by 97 publications

References 49 publications

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

Emotion Recognition in Immersive Virtual Reality: From Statistics to Affective Computing

Flow and Immersion in Video Games: The Aftermath of a Conceptual Challenge

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