Estimating the sensorimotor components of cybersickness

Area of Science:

• Human-Computer Interaction
• Neuroscience
• Biomedical Engineering

Background:

• The increasing use of virtual reality (VR) necessitates understanding cybersickness, a common side effect.
• Individual differences in cybersickness are significant but not fully explained by single factors.
• Existing research often overlooks the complex interplay of sensorimotor factors in cybersickness.

Purpose of the Study:

• To investigate the relationship between cybersickness and multiple sensorimotor processing measures.
• To develop a predictive model for cybersickness based on sensorimotor performance.
• To identify key sensorimotor factors contributing to individual cybersickness variability.

Main Methods:

• Conducted a battery of tests measuring balance control, vection responses, and vestibular sensitivity.
• Administered VR exposure and measured subsequent cybersickness.
• Utilized principal components regression to model cybersickness based on sensorimotor data.

Main Results:

• The predictive model explained 37% of the variability in cybersickness.
• Balance control measures, particularly sway path length during vection, were significant predictors.
• Sway path length during vection inversely predicted cybersickness, accounting for 7.5% of the variance.
• Vection strength and vestibular sensitivity were not significant predictors.

Conclusions:

• Balance control plays a crucial role in cybersickness, but the relationship is complex.
• Sensory reweighting may contribute to cybersickness.
• Further research is needed to identify other factors influencing cybersickness.
• This approach can inform personalized VR content recommendations based on sensorimotor profiles.