Motion sickness (MS) affects nearly all people, but motion sickness susceptibility and elicitation of symptoms vary by individuals. With different levels of autonomous driving coming in years, MS would affect even more people. In this research, few predictors like sleep duration, exercise frequency and intensity, blood oxygen level and menstrual cycle timing are identified for susceptibility prediction. With current wearable technologies, these predictors can be monitored and tracked. The hypothesis is that it is possible to use wearable systems to predict susceptibility. User tests are performed to test an algorithm that is made up of the found predictors. The result shows positive correlations but not significant enough. Possible improvement ideas are then listed for further improvement in the prediction.

Tutor: Prof. Stephen J. Wang