Improving motion sickness severity classification through multi-modal data fusion

Head mounted displays (HMD) may prove useful for synthetic training and augmentation of military C5ISR decisionmaking. Motion sickness caused by such HMD use is detrimental, resulting in decreased task performance or total user dropout. The genesis of sickness symptoms is often measured using paper surveys, which are difficult to deploy in live scenarios. Here, we demonstrate a new way to track sickness severity using machine learning on data collected from heterogeneous, non-invasive sensors worn by users who navigated a virtual environment while remaining stationary in reality. We discovered that two models, one trained on heterogeneous sensor data and another trained only on electroencephalography (EEG) data, were able to classify sickness severity with over 95% accuracy and were statistically comparable in performance. Greedy feature optimization was used to maximize accuracy while minimizing the feature subspace. We found that across models, the features with the most weight were previously reported in the literature as being related to motion sickness severity. Finally, we discuss how models constructed on heterogeneous vs homogeneous sensor data may be useful in different real-world scenarios.