Virtual Reality Adaptation using Electrodermal Activity to Support User Experience (doi:10.18419/darus-2820)

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Virtual Reality Adaptation using Electrodermal Activity to Support User Experience

doi:10.18419/darus-2820

DaRUS

2022-04-27

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Chiossi, Francesco; Welsch, Robin; Villa, Steeven; Chuang, Lewis; Mayer, Sven, 2022, "Virtual Reality Adaptation using Electrodermal Activity to Support User Experience", https://doi.org/10.18419/darus-2820, DaRUS, V1

Virtual Reality Adaptation using Electrodermal Activity to Support User Experience

doi:10.18419/darus-2820

Chiossi, Francesco (Universität München (LMU))

Welsch, Robin (Universität München (LMU))

Villa, Steeven (Universität München (LMU))

Chuang, Lewis (Humans and Technology, Institute for Media Research, Faculty of Humanities, Chemnitz University of Technology, Chemnitz)

Mayer, Sven (Universität München (LMU))

251654672

DaRUS

Chiossi, Francesco

Chiossi, Francesco

Chiossi, Francesco

2022-04-21

https://doi.org/10.18419/darus-2820

Computer and Information Science, HCI, Human-Computer Interaction, Virtual Reality, Adaptive System

<p>We report an experiment (N=18) where participants where engaged in a dual task setting in a Social VR (Virtual Reality) scenario. We present a physiologically-adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of not-playable characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal the task complexity.</p> <p>Specifically we make available physiological (Electrodermal Activity - EDA, Electroencephalography - EEG; Electrocardiography - ECG) , behavioral and questionnaires data and lastly, the analysis code.</p>

Users interested in reproducing the results can follow the methodology as reported in the paper and the analysis code as reported in the Python script (" Step_02.") in the Files section.

Chiossi, F.; Welsch, R.; Villa, S.; Chuang, L.; Mayer, S. Virtual Reality Adaptation using Electrodermal Activity to Support User Experience. Big Data Cogn. Comput. 2022.

Chiossi, F.; Welsch, R.; Villa, S.; Chuang, L.; Mayer, S. Virtual Reality Adaptation using Electrodermal Activity to Support User Experience. Big Data Cogn. Comput. 2022.

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DataSet.7z

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Dataset containing data for each participant. Specifically included are : 1. Slope of the EDA tonic in -adaptation.csv ; 2. Raw Electrocardiogram (ECG) data in -ECG.csv; 3. Raw Electroencephalogram (EEG) data in -EEG.csv acquired from Value1 : Fz, P3, P, P4, PO7, Oz and PO8 electrodes. 4. Feedback presented during the N-Back task in -feedback.csv. 5. Count and description of each Not-Playable Characters in the VR Environment, relevant to the visual detection task in -flow.csv. 6. Start and End of each condition, including Baseline definition in -state.csv 7. Count of Not-Playable Characters present in the VR scene across the duration of the experiment in -visitorCount.csv .

application/x-7z-compressed

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Step_01.py

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Code to preprocess the raw dataset (performance, subjective and Electrodermal activity data)

text/x-python

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Step_02.ipynb

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Code for Statistical analysis of preprocessed data (Jupyter Notebook file)

application/x-ipynb+json