The primary objective of this thesis is to develop a new model that accurately predicts vertical mo- tion sickness by closely mirroring the neural dynamics and statistical properties of the otolith pathway. Unlike existing models, our approach incorporates elements such as noise, nonlinearities, and realistic sensory data, enhancing the accuracy and biological relevance of motion sickness predictions. This re- search aims to create a biologically plausible approach that derives a proxy metric for Motion Sickness Incidence (MSI). This metric will naturally emerge from sensory conflict, providing a more accurate prediction of vertical motion sickness and contributing to the development of systems designed to mit- igate its effects. This has become increasingly critical with the growing prevalence of self-driving cars and virtual reality systems, where effectively addressing motion sickness is of great value. To achieve this goal, we developed a novel model from the ground up, incorporating detailed dynam- ics and statistical properties of otolith hair cells and afferents in the inner ear. Three spiking models were developed based on Leaky-Integrate-and-Fire, Hodgkin-Huxley, and Generalized Linear Model frameworks. Each model presents unique advantages in capturing neural behavior. Our findings indi- cate that while all models offer valuable insights, the Leaky-Integrate-and-Fire model most effectively captures the dynamics, statistics, and temporal characteristics of otolith afferents. The spiking models were used in a scheme that improves upon existing models in three ways. Firstly, and most fundamentally, we added noise and probability elements to our modeling. This makes the models more realistic by reflecting the natural randomness and variations in human behavior and per- ception. By more closely mimicking real-world conditions, our models better represent how humans perceive and respond to stimuli, making the simulations more accurate overall. Additionally, we incorporate nonlinearities to accurately represent sensory saturation and have imple- mented a realistic organ definition, including hair cell tuning. The model relies on actual sensory data rather than simply aligning with subjective experimental results, allowing for a more accurate and bio- logically relevant simulation of sensory processing. Lastly, the spiking models are integrated within a framework that utilizes Bayesian inference to esti- mate vertical acceleration, taking into account the inherent noise in the brain and aligning with modern neuroscience theories. By bridging the Free Energy Principle with the Sensory Conflict Theory, our model provides a more robust framework for understanding and predicting motion sickness. The model gives results that are representative in terms of human motion perception sensitivity, while also being in line with current knowledge about frequency and amplitude dependence of Motion Sickness Incidence. The resulting model gives us insights into how otolith dynamics influence motion sickness and is a proposal on how the brain resolves estimating vertical motion perception. Using different model configurations we were able to create a model that estimates motion accurately and propose a proxy metric for motion sickness incidence. This model is created in a modular way and can be extended on the basis of new objectives or new research findings. Additionally, with further enhancements, it holds potential applications in validating otolith theories on statistics and information transmission characteristics and even in the development of prostheses.

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.