Electrical and Computer Engineering
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Browsing Electrical and Computer Engineering by Author "Allison, Robert"
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Item Open Access Depth Perception Under Scaled Motion Parallax in Virtual Reality(2023-08-04) Teng. Xue; Allison, RobertThis thesis investigates the impact of mismatch between virtual and physical motion on the perception of object shape. We varied the gain between virtual and physical head motion and measured the effect on depth, distance and shape perception. Our results showed that under monocular viewing, both depth and distance settings decreased with increasing gain, especially at close distances. The average effect sizes of gain were up to -0.061 m/gain unit and -0.40 m/gain unit on depth and distance, respectively, when measured on a standard fold with depth of 1 m. Observers experienced less distortions than predicted from a geometric model and very little depth distortion (not statistically significant effect of gain) under binocular viewing. The distance distortion caused by gain was reduced by up to 56.6% compared to monocular viewing. Binocular cues to depth and distance and large distance (at 6 m) enhance humans’ tolerance to visual and kinesthetic mismatch.Item Open Access Modelling the Relationship Between Physiological Measures of Motion Sickness(2023-08-04) Shodipe, Oluwaseyi Elizabeth; Allison, RobertCar sickness is anticipated to occur more frequently in self-driving vehicles because of their design. This thesis involved an investigation using machine learning techniques with physiological measures to detect and predict the severity of car sickness in real-time every two minutes. A total of 40 adults were exposed to two conditions, each involving a 20-minute ride on a motion-base simulator. Car sickness incidence and severity were subjectively measured using the Fast Motion Sickness (FMS) and Simulator Sickness Questionnaire (SSQ). Car sickness symptom was successfully elicited in 31 participants (77.5%) while avoiding simulator sickness. Results showed that head movement had the strongest relationship with car sickness, and there was a moderate correlation between heart rate and skin conductance. The machine learning models revealed a medium correlation between the physiological measures and the FMS scores. An acceptable classification score distinguishing between motion-sick and non-motion-sick participants was found using the random forest model.