In the near future, travelling in vehicles will no longer be in regular vehicles, but in automated vehicles. The share of automated vehicles is predicted to increase significantly within 20 years. Passengers in automated vehicles will engage in non-driving tasks, such as sleeping, reading, working or just otherwise spending time on their phone. This will result in motion sickness becoming more prevalent, as passengers will no longer pay attention to the road. Therefore, there is a need for research in motion sickness. To further our understanding of motion sickness and possible mitigation strategies, mathematical models of motion sickness need to be developed. The temporal dynamics of motion sickness can be captured in the so called 'Oman model' \cite{Oman90}. However, most literature use group averaged parameters and motion sickness incidence to describe motion sickness. These methods do not capture well enough how individuals respond to sickening stimuli, as recent studies showed that individuals have strongly varying responses to various frequencies. Only, estimating individual motion sickness parameters is costly thus far, requiring multiple experiments to estimate the parameters. This study explains an optimal experiment design, where the input is varied in real-time closed loop manner such that the information content in the input is maximized for estimation of parameters, rusulting in the fact that individual motion sickness parameters could be estimated in a single experiment. Results show that on average, within the first 63 minutes, most parameter estimations have converged. The resulting RMSE is 1.06 on the MISC scale, comparing to other literature. This shows that the frequency and temporal dynamics of motion sickness and an individual level can be estimated at a drastically faster rate than previous methods. To our knowledge this is the first use of optimal experiment design techniques to asses the dynamics of human responses to stimuli in general, which is an important milestone for cybernetics research.