Classification of Motion Sickness Levels using Multimodal Biosignals in Real Driving Conditions

Motion sickness is an unpleasant physiological response to situations involving the perception of motion. Research on motion sickness focuses on its manifestation by analyzing biosignals to observe physiological changes coinciding with the perception of motion sickness. Meanwhile, multimodal data fusion has gained attention for its ability to reflect the multimodality of real-life tasks and enhance the robustness of machine learning models. In this study, we aimed to find a deep learning-based multimodal framework for integrative analysis of multiple biosignals with the highest performance in classifying the level of carsickness. To do so, we first generated a dataset consisting of five different types of biosignals collected under real driving conditions: electroencephalogram (EEG), electrocardiogram (ECG), respiration (RESP), photoplethysmogram (PPG), and galvanic skin response (GSR). Then, we compared six deep learning-based unimodal classification models which have shown competency in signal classification. Lastly, we compared four different fusion methods for multimodal classification frameworks using either all five biosignals or three signals, which include RESP, ECG, and PPG. As a result, we found out that the fusion method combining self-attention and the tensor fusion network outperformed other unimodal and multimodal models with categorical accuracy of 76.26 % regardless of the number of biosignals used.