Data underlying the publication: Visually Induced Motion Sickness Correlates with On-Road Car Sickness while Performing a Visual Task
DOI: 10.4121/5f54188f-9e47-4ac7-8cf3-2ebb852bdf15
Dataset
Licence CC0
Abstract—Previous literature suggests that the motion sickness susceptibility questionnaire (MSSQ) is inadequate for prediction of motion sickness under naturalistic driving conditions. In this study, we investigated whether visually induced motion sickness using a virtual reality head-set could be used as a quick and reliable way to predict participant susceptibility. We recruited 22 participants to complete a two-part experiment. In randomised order, we determined their susceptibility to visual motion sickness and their susceptibility to car sickness. To determine visual susceptibility, the visual scene was sequentially rotated at constant velocity around an earth-vertical yaw axis and rolled about the nasiooccipital axis, in 30 s intervals. Car sickness, on the other hand, was elicited under completely naturalistic conditions, being driven in the back-seat of a car in the city of Delft, performing a visual task on a laptop. Sickness ratings were collected at regular intervals in both parts of the experiment.
We found that the frequencies excited by naturalistic driving are very low, which has important consequences for motion sickness modelling and mitigation in automated vehicles.
We found that individual car sickness correlated positively with visual motion sickness. This indicates that both are influenced by a common sickness susceptibility factor. Car sickness correlated similarly with visual motion sickness and MSSQ. Overall, our results indicate that combining measurements of sickness responses to a visual stimulus and MSSQ can yield a reliable method for determining individual sickness susceptibility. To this end the visual stimulus and the weighting with MSSQ responses can be refined using a much larger sample and considering additional visual conditions in driving.
History
- 2025-01-28 first online, published, posted
Publisher
4TU.ResearchDataFormat
Excel, *.xlsxFunding
- HiDrive (grant code 101006664) [more info...] Horizon 2020
Organizations
TU Delft, Faculty of Mechanical Engineering, Department of Cognitive roboticsDATA
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