Investigating the Potential of Multi-Task Learning for Enhancing SSQ Prediction

Deep learning is widely used to forecast cybersickness to inform mitigation efforts. However, prior work mostly focused on predicting the Fast Motion Sickness score, which does not provide detailed information on cybersickness symptoms. To fill this gap, this paper focuses on forecasting cybersickness symptoms and evaluates the effectiveness of multi-task learning. The results show that multitask learning does not always outperform the traditional single-task learning approach in this domain.