AI for preventive support with motion sickness.


Our strategy consulting focused on detecting motion sickness at an early stage using intelligent driver assistance systems. The challenge was to use sensor measurements and rates of change to make accurate predictions and increase safety on the roads.
We have actively participated in research to improve accuracy in the early detection of motion sickness. The data basis for our models was based on extensive sensor measurements such as face temperature and color as well as numerous rates of change to optimize this prediction problem.
Our expertise was particularly applied to this complex multi-category classification problem. The development combined machine learning pipelines with nested binary classification models based on best practices, feature engineering and latest research results to train an effective prediction model.
By integrating this preventive AI solution into a vehicle's on-board computer, the driver received reliable information about their motion sickness condition. This made it possible to react to changes in good time and take a break if necessary to ensure safety.
Our extensive experience and know-how led to a significant improvement in the accuracy and reliability of the prediction. This use case not only shows the effectiveness of our solution for preventive support in motion sickness, but also demonstrates the knowledge transfer of AI algorithms between industries to develop innovative solutions.


Would you like to learn more about the opportunities of predictions with AI? We would like to invite you to a non-binding initial consultation and show you further possible applications for your company.