Artificial neural network for modelling of the correlation between lateral acceleration and head movement in a motion sickness study

• Main Authors: Hassan, N. (Author), Mohammed Ariff, M.H (Author), Saruchi, S.A (Author), Wahid, N. (Author), Zamzuri, H. (Author)
• Format: Article
• Language: English
• Published: Institution of Engineering and Technology 2019
• Subjects: Correlation modeling; Diseases; Lateral acceleration; Mathematical equations; Mathematical representations; Modelling process; Motion control; Motion sickness; Neural networks; Number of hidden neurons; Vehicle motion controls; Vehicles
• Online Access: View Fulltext in Publisher; View in Scopus

 Motion sickness (MS) usually occurs when travelling in a moving vehicle, and especially experienced by the passengers compared to the driver. The difference in their head movements with respect to the direction of lateral acceleration affects the MS severity level. When experiencing curvature, the passengers normally tilt their head in the same direction as the lateral acceleration, while the driver tilts his/her head against it. This study proposes a correlation model between the lateral acceleration of the vehicle and the head movements of the driver and a passenger via an artificial neural network. Experimental datasets were used in the modelling process. The influence of the number of hidden neurons with respect to the model accuracy has also been investigated. Then, the correlation from the model was expressed as a mathematical equation. This mathematical representation model can be beneficial in the design of vehicle motion control systems in order to mitigate the MS effect. © 2019 Institution of Engineering and Technology. All rights reserved.