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

• Published in: IET intelligent transport systems Vol. 13; no. 2; pp. 340 - 346
• Main Authors: Saruchi, Sarah ‘Atifah, Mohammed Ariff, Mohd Hatta, Zamzuri, Hairi, Hassan, Nurhaffizah, Wahid, Nurbaiti
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.02.2019
• Subjects: acceleration; artificial neural network; control engineering computing; correlation model; head movement; health care; hidden neurons; lateral acceleration; mathematical equation; mathematical representation model; mechanical engineering computing; modelling process; motion control; motion sickness study; moving vehicle; MS effect mitigation; MS severity level; neural nets; Research Article; vehicle dynamics; vehicle motion control systems; correlation model; artificial neural network; motion sickness study; MS effect mitigation; lateral acceleration; vehicle motion control systems; head movement; MS severity level; vehicle dynamics; hidden neurons; mathematical representation model; acceleration; mechanical engineering computing; motion control; moving vehicle; modelling process; control engineering computing; health care; neural nets; mathematical equation
• ISSN: 1751-956X; 1751-9578; 1751-9578
• DOI: 10.1049/iet-its.2018.5264

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.