Airflow is recognized as an effective method for inducing the illusion of self-motion (vection) and reducing motion sickness in virtual reality. However, the quantitative relationship between virtual motion and the airflow perceived as consistent with it has not been fully explored. To address this gap, this study conducted three experiments. In Experiment 1, we carried out a series of cross-modal matching tasks to establish the relationship between the speed of virtual motion and the airflow speed perceived as consistent with it, revealing a strong linear correlation. In Experiment 2, we introduced the concept of an "Airflow Gradient" to simulate the bodily sensation of curvilinear motion and examined the relationship between the radius and angular velocity of the motion and the difference in airflow speed between the left and right sides. The results indicated a linear relationship between the radius and the left-right airflow speed difference, while the angular velocity showed a near-quadratic pattern, similar to the centripetal acceleration formula. Based on these findings, Experiment 3 developed a dynamic airflow scheme and compared it with constant airflow and no-airflow conditions during locomotion tasks in a complex urban environment. The results demonstrated that dynamic airflow, which ensures consistency between visual and bodily vection, further reduces motion sickness, enhances presence, and provides a more natural and consistent virtual motion experience.

Semisupervised scheme has emerged as a popular strategy in the machine learning community due to the expensiveness of getting enough labeled data. In this paper, a semisupervised incremental support vector machine (SE-INC-SVM) algorithm based on neighborhood kernel estimation is proposed. First, kernel regression is constructed to estimate the unlabeled data from the labeled neighbors and its estimation accuracy is discussed from the analogy with tradition RBF neural network. The incremental scheme is derived to improve the learning efficiency and reduce the computing time. Simulations for manual data set and industrial benchmark-penicillin fermentation process demonstrate the effectiveness of the proposed SE-INC-SVM method.