Development and Validation of Pictographic Scales for Rapid Assessment of Affective States in Virtual Reality

Krüger, Christian; Kojić, Tanja; Meier, Luis; Möller, Sebastian; Voigt-Antons, Jan‐Niklas

doi:10.1109/qomex48832.2020.9123100

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…This suggests a need for immersive affective self-report tools to assess VR evoked emotions, preferably at multiple instances during the VR experience itself (Bouchard et al, 2004;Regal et al, 2019;Oliveira et al, 2020). Recent studies showed that the inclusion of an affective rating tool in the VE can speed up user response by almost a factor of five compared to paper and pencil methods (Krüger et al, 2020), while the superposition of a two-dimensional affective response grid over 360 • videos affords continuous affective ratings (Voigt-Antons et al, 2020;Xue et al, 2020). It has also been shown that self-report tools are less invasive and yield more reliable results when they used inside a VR, compared to their application outside the VR (Schwind et al, 2019;Putze et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…An essential requirement for these type of studies is the availability of validated and efficient tools for the assessment of affective responses to VR experiences (Oliveira et al, 2018). Preferably, these instruments should be able to unobtrusively blend into the VE so that users do not lose their sense of immersion when they need to step out of the VR to give a response (Regal et al, 2019;Krüger et al, 2020;Voigt-Antons et al, 2020). The recently introduced EmojiGrid is a viable candidate since it is intuitive and language independent (and therefore probably requires minimal cognitive effort: Kaneko et al, 2018b;Toet et al, 2018;Toet and van Erp, 2019;Voigt-Antons et al, 2020), and since it can easily be embedded in a VE.…”

Section: Introductionmentioning

confidence: 99%

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An Immersive Self-Report Tool for the Affective Appraisal of 360° VR Videos

Toet

Heijn

Brouwer

et al. 2020

Front. Virtual Real.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Immersive Self-Report Tool for the Affective Appraisal of 360° VR Videos

Toet

Heijn

Brouwer

et al. 2020

Front. Virtual Real.

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“…This dataset contains 40 music videos. A subset of 16 videos selected during a previous study (Kruger et al, 2020) were used in the experiment. Two additional videos were used for training trials.…”

Section: Stimulimentioning

confidence: 99%

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Pinilla

Voigt-Antons

García

et al. 2023

Front. Virtual Real.

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This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%.

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“…Thus, considering the aforementioned therapies, researches are working twoards the development of tools which assesses the user experience. Both the therapeutical context, and the individuals affective state are taken into account (Krüger et al, 2020).…”

Section: Games Virtual Reality Affective Robotics and Therapiesmentioning

confidence: 99%

Affective computing in the context of music therapy: a systematic review

Santana

Lima

Torcate

et al. 2021

RSD

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Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

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Development and Validation of Pictographic Scales for Rapid Assessment of Affective States in Virtual Reality

Cited by 5 publications

References 17 publications

An Immersive Self-Report Tool for the Affective Appraisal of 360° VR Videos

An Immersive Self-Report Tool for the Affective Appraisal of 360° VR Videos

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Affective computing in the context of music therapy: a systematic review

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