Modelling the Relationship Between Physiological Measures of Motion Sickness

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dc.contributor.advisorRobert Allison
dc.contributor.authorOluwaseyi Elizabeth Shodipe
dc.date.accessioned2023-08-04T15:23:03Z
dc.date.available2023-08-04T15:23:03Z
dc.date.issued2023-08-04
dc.date.updated2023-08-04T15:23:02Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractCar 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.
dc.identifier.urihttps://hdl.handle.net/10315/41402
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectMedicine
dc.subjectPsychology
dc.subject.keywordsAnalysis of Variance
dc.subject.keywordsElectrocardiogram
dc.subject.keywordsElectrodermal Activity
dc.subject.keywordsElectrooculogram
dc.subject.keywordsFast Motion Sickness
dc.subject.keywordsHigh-Frequency Power
dc.subject.keywordsHeart Rate Variability
dc.subject.keywordsInertial Measurement Unit
dc.subject.keywordsLow-Frequency Power
dc.subject.keywordsMotion Sickness
dc.subject.keywordsMotion Sickness Susceptibility Questionnaire
dc.subject.keywordsNon-Driving Related Task
dc.subject.keywordsPensacola Diagnostic Index
dc.subject.keywordsSkin Conductance Response
dc.subject.keywordsSimulator Sickness Questionnaire
dc.subject.keywordsVisual-Induced Motion Sickness
dc.subject.keywordsVirtual Reality
dc.titleModelling the Relationship Between Physiological Measures of Motion Sickness
dc.typeElectronic Thesis or Dissertation

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