%0 Journal Article %1 feigl2019toappearmoves %A Feigl, Tobias %A Roth, Daniel %A Gradl, Stefan %A Wirth, Markus Gerhard %A Philippsen, Michael %A Latoschik, Marc Erich %A Eskofier, Bjoern %A Mutschler, Christopher %D 2019 %J IEEE Transactions on Visualization and Computer Graphics (TVCG) %K droth myown %T Sick Moves! Motion Parameters as Indicators of Simulator Sickness %X We explore motion parameters, more specifically gait parameters, asan objective indicator to assess simulator sickness in Virtual Reality(VR). We discuss the potential relationships between simulator sick-ness, immersion, and presence. We used two different camera pose(position and orientation) estimation methods for the evaluation ofmotion tasks in a large-scale VR environment: a simple model andan optimized model that allows for a more accurate and natural map-ping of human senses. Participants performed multiple motion tasks(walking, balancing, running) in three conditions: a physical realitybaseline condition, a VR condition with the simple model, and a VRcondition with the optimized model. We compared these conditionswith regard to the resulting sickness and gait, as well as the perceivedpresence in the VR conditions. The subjective measures confirmedthat the optimized pose estimation model reduces simulator sick-ness and increases the perceived presence. The results further showthat both models affect the gait parameters and simulator sickness,which is why we further investigated a classification approach thatdeals with non-linear correlation dependencies between gait param-eters and simulator sickness. We argue that our approach could beused to assess and predict simulator sickness based on human gaitparameters and we provide implications for future research

@article{feigl2019toappearmoves, abstract = {We explore motion parameters, more specifically gait parameters, asan objective indicator to assess simulator sickness in Virtual Reality(VR). We discuss the potential relationships between simulator sick-ness, immersion, and presence. We used two different camera pose(position and orientation) estimation methods for the evaluation ofmotion tasks in a large-scale VR environment: a simple model andan optimized model that allows for a more accurate and natural map-ping of human senses. Participants performed multiple motion tasks(walking, balancing, running) in three conditions: a physical realitybaseline condition, a VR condition with the simple model, and a VRcondition with the optimized model. We compared these conditionswith regard to the resulting sickness and gait, as well as the perceivedpresence in the VR conditions. The subjective measures confirmedthat the optimized pose estimation model reduces simulator sick-ness and increases the perceived presence. The results further showthat both models affect the gait parameters and simulator sickness,which is why we further investigated a classification approach thatdeals with non-linear correlation dependencies between gait param-eters and simulator sickness. We argue that our approach could beused to assess and predict simulator sickness based on human gaitparameters and we provide implications for future research}, added-at = {2019-06-17T10:43:44.000+0200}, author = {Feigl, Tobias and Roth, Daniel and Gradl, Stefan and Wirth, Markus Gerhard and Philippsen, Michael and Latoschik, Marc Erich and Eskofier, Bjoern and Mutschler, Christopher}, biburl = {https://www.bibsonomy.org/bibtex/24d2c943bb9dbc85e4cd69f9695469f22/rothnroll}, interhash = {6cf4437dd4277aa41f012cf72be17ceb}, intrahash = {4d2c943bb9dbc85e4cd69f9695469f22}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, keywords = {droth myown}, timestamp = {2019-10-30T09:46:55.000+0100}, title = {Sick Moves! Motion Parameters as Indicators of Simulator Sickness}, year = 2019 }