Personalizing motion sickness models: estimation and statistical modeling of individual-specific parameters
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View abstract on PubMed
Summary
This summary is machine-generated.Individualized motion sickness models improve prediction accuracy in automated vehicles. This personalized approach captures unique user susceptibility, enhancing safety and comfort during non-driving activities.
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.
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