Internship | Seeing Discomfort: Machine Learning Approaches to Motion Sickness Detection in Video Data

About this position

Many people experience motion sickness when traveling, yet there is still no good way to detect it without asking passengers directly. In this assignment, it is explored whether video recordings can reveal the subtle signs of discomfort and motion sickness, such as changes in posture, head movement, or facial expressions. As a master’s student, you will investigate this possibility using machine learning techniques on existing datasets. If successful, your work could help for affordable solutions that improve comfort for passengers in automated vehicles.

What will be your role?

You will conduct a feasibility study to detect motion sickness from RGB video data. Using video recordings from three earlier TNO studies, you will extract features related to, for instance, head motion, posture, and facial muscle activation (action units) with the help of open-source libraries. These features will be combined with participa...