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

DOI

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.

Specifically we make available physiological (Electrodermal Activity - EDA, Electrocardiography - ECG) , behavioral, motion tracking, questionnaires data and lastly, the analysis code.

Identifier
DOI https://doi.org/10.18419/darus-3139
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3139
Provenance
Creator Dietz, Dennis ORCID logo; Oechsner, Carl ORCID logo; Ou, Changkun ORCID logo; Chiossi, Francesco ORCID logo; Sarto, Fabio ORCID logo; Mayer, Sven ORCID logo; Butz, Andreas ORCID logo
Publisher DaRUS
Contributor Chiossi, Francesco; Dietz, Dennis; Ou, Changkun
Publication Year 2022
Funding Reference DFG 251654672
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Chiossi, Francesco (LMU Munich); Dietz, Dennis (LMU Munich); Ou, Changkun (LMU Munich)
Representation
Resource Type Dataset
Format application/x-7z-compressed; text/plain; video/mp4
Size 15341111; 44586230; 4289; 45800794
Version 1.0
Discipline Other