Quantifying the Relative Contributions of Exposure Time, ECG, GSR, and Vibration-Derived Features for Motion Sickness Prediction

Motion sickness is expected to become more prevalent in road transportation as passengers increasingly engage in non-driving-related activities under automated driving. This study quantified the relative contributions of exposure time (discrete trial index), physiological signals, and vibration-derived features to motion sickness prediction under controlled road vibration. Thirty-nine participants were exposed to four road profiles reproduced on a motion simulator while electrocardiography (ECG), galvanic skin response (GSR), triaxial head acceleration, and motion sickness ratings were recorded across ten 2 min trials per session during a reading task. Three dataset configurations were compared: exposure time only (type 1), physiological and vibration-derived features only (type 2), and the combined (type 3) dataset. Motion sickness prediction was evaluated using support vector classification, random forest, and XGBoost, and permutation importance was used to assess feature contribution. Across all classifiers, exposure time was ranked as the most important feature, followed by overall root-mean-square, while ECG and GSR contributed at lower levels. The combined dataset achieved the best performance, reaching 77% classification accuracy and R2 values up to 0.49. Leave-one-subject-out (LOSO) cross-validation further confirmed the feature importance ranking under subject-level evaluation, while physiological features showed near-zero importance for unseen participants, suggesting their contributions are largely individual-specific.