Personalizing motion sickness models: estimation and statistical modeling of individual-specific parameters.

Area of Science:

• Human-computer interaction
• Automotive engineering
• Human factors psychology

Background:

• Automated vehicles shift users from drivers to passengers, leading to non-driving activities and potential motion sickness.
• Discrepancies between expected and perceived motion in vehicles and simulators cause sickness, necessitating improved motion control.
• Individual differences in motion sickness susceptibility require personalized countermeasures.

Purpose of the Study:

• To develop and validate a personalized framework for predicting motion sickness in automated vehicles.
• To capture individual differences in motion sickness susceptibility across various motion and visual conditions.
• To enhance the accuracy of motion sickness prediction models for personalized interventions.

Main Methods:

• Combined a group-averaged sensory conflict model with an individualized Accumulation Model (AM).
• Validated the framework using three datasets from vehicle and simulator experiments under passive motion conditions.
• Utilized an individualized AM (AM2) with two parameters (gain K1 and time constant T1) to model individual responses.

Main Results:

• The AM2 model achieved an average improvement factor of 1.7 in fitting individual motion sickness responses compared to the group-averaged AM0 model.
• The AM2 model accurately modeled individual sickness responses across diverse motion and vision conditions using personalized parameters.
• A Gaussian mixture model of parameter distributions predicted motion sickness in an unseen dataset with an average RMSE of 0.47.

Conclusions:

• The proposed individualized Accumulation Model (AM2) framework effectively captures personal motion sickness susceptibility in automated vehicles.
• This personalized approach enhances prediction accuracy and reduces the need for extensive population-level testing.
• The framework offers a robust solution for developing tailored motion control strategies to mitigate sickness in automated vehicle occupants.