Dataset and Analysis for "Walk This Beam: Impact of Different VR Balance Training Strategies and Height Exposure on Performance and Physiological Arousal" (doi:10.18419/darus-3139)

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Dataset and Analysis for "Walk This Beam: Impact of Different VR Balance Training Strategies and Height Exposure on Performance and Physiological Arousal"

doi:10.18419/darus-3139

DaRUS

2022-10-05

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Dietz, Dennis; Oechsner, Carl; Ou, Changkun; Chiossi, Francesco; Sarto, Fabio; Mayer, Sven; Butz, Andreas, 2022, "Dataset and Analysis for "Walk This Beam: Impact of Different VR Balance Training Strategies and Height Exposure on Performance and Physiological Arousal"", https://doi.org/10.18419/darus-3139, DaRUS, V1

Dataset and Analysis for "Walk This Beam: Impact of Different VR Balance Training Strategies and Height Exposure on Performance and Physiological Arousal"

doi:10.18419/darus-3139

Dietz, Dennis (LMU Munich)

Oechsner, Carl (LMU Munich)

Ou, Changkun (LMU Munich)

Chiossi, Francesco (LMU Munich)

Sarto, Fabio (University of Padova)

Mayer, Sven (LMU Munich)

Butz, Andreas (LMU Munich)

251654672

DaRUS

Chiossi, Francesco

Dietz, Dennis

Ou, Changkun

Chiossi, Francesco

2022-09-14

https://doi.org/10.18419/darus-3139

Computer and Information Science, Virtual Reality, Physiological Arousal, Balance Control, HCI, Human-Computer Interaction

<p>Dynamic balance is an essential skill for the human upright gait; therefore, regular balance training can improve postural control and reduce the risk of injury. Even slight variations in walking conditions like height or ground conditions can significantly impact walking performance. Virtual reality is used as a helpful tool to simulate such challenging situations. However, there is no agreement on design strategies for balance training in virtual reality under stressful environmental conditions such as height exposure. We investigate how two different training strategies, imitation learning, and gamified learning, can help dynamic balance control performance across different stress conditions. Moreover, we evaluate the stress response as indexed by peripheral physiological measures of stress, perceived workload, and user experience. Both approaches were tested against a baseline of no instructions and against each other. Thereby, we show that a learning-by-imitation approach immediately helps dynamic balance control, decreases stress, improves attention focus, and diminishes perceived workload. A gamified approach can lead to users being overwhelmed by the additional task. Finally, we discuss how our approaches could be adapted for balance training and applied to injury rehabilitation and prevention.</p> Specifically we make available physiological (Electrodermal Activity - EDA, Electrocardiography - ECG) , behavioral, motion tracking, questionnaires data and lastly, the analysis code.

Dennis Dietz, Carl Oechsner, Changkun Ou, Francesco Chiossi, Fabio Sarto, Sven Mayer, and Andreas Butz. 2022. Walk This Beam: Impact of Different Balance Assistance Strategies and Height Exposure on Performance and Physiological Arousal in VR. In Proceedings of ACM Conference (VRST’22). ACM, New York, NY, USA, 11 pages

Dennis Dietz, Carl Oechsner, Changkun Ou, Francesco Chiossi, Fabio Sarto, Sven Mayer, and Andreas Butz. 2022. Walk This Beam: Impact of Different Balance Assistance Strategies and Height Exposure on Performance and Physiological Arousal in VR. In Proceedings of ACM Conference (VRST’22). ACM, New York, NY, USA, 11 pages

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analysis.7z

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The analysis file contains Jupyter Notebook file for analysis of the different dependent variables collected in the study: 1. Data Cleaning (Broken data and Artifacts) 2. Dataframe creation 3. Accounting for Missing data 4. ECG analysis 5. Electrodermal activity analysis 6. Analysis for Presence Questionnaire (IPQ) 7. Analysis for NASA TLX Questionnaire 8. Analysis for PACES questionnaire 10. Data Visualization (Python 3.9 file). Statistical analysis is performed in R.

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data.7z

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Full dataset containing raw data per participant. Each participant folder contains: 1. ECG data (ECG.csv) 2. Electrodermal Activity data (EDA.csv) 3.Motion tracking data (Head, Left Foot, Right Foot, Left Hand, Right Hanf, Pelvis). 4. Movement of the tutorial ghost for the Imitation condition (Clone.csv). At the beginning of each file it is logged which condition (Gamification, Imitation and No instructions, Low\High Tide). This information is also available in the state.csv .

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Software_Requirements.txt

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List of required Python Packages for running the statistical analysis for all dependent variables

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Video.mp4

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mp4 file showing the experimental paradigm and the VR environment

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