Prediction of Individual Motion Sickness of Bus Passengers Using a 6DoF-SVC Model Driven by Handheld Tablet Motion During Visual Tasks

Abstract

Motion sickness is a common issue for passengers in public transportation, particularly when engaging in visual tasks using handheld devices. This study investigates the feasibility of capturing individual motion sickness progression in bus passengers using a six-degree-of-freedom subjective vertical conflict (6DoF-SVC) model driven solely by motion data from a handheld tablet. Fifteen participants performed a visual search task on a tablet while riding a public bus, during which motion data from both a head-mounted inertial measurement unit (IMU) and a tablet-integrated IMU were recorded. Individual motion sickness severity was assessed using the Motion Illness Symptoms Classification (MISC). Model parameters were individually identified under different data availability conditions, and estimation accuracy was evaluated using mean absolute error. The results showed that estimation accuracy using tablet motion was not significantly different from that using head motion, and that parameter estimation using data up to MISC $\ge 3$ achieved accuracy comparable to that obtained using the entire dataset. Canonical correlation analysis revealed high correlations between head and tablet motion. These findings demonstrate that high-accuracy, individualized estimation of motion sickness severity is achievable in a real-world bus environment using only handheld device motion data, highlighting the potential for practical, sensor-light personalized monitoring systems.