Open AccessArtificial neural network for modelling of the correlation between lateral acceleration and head movement in a motion sickness study
Author(s) -
Saruchi Sarah ‘Atifah,
Mohammed Ariff Mohd Hatta,
Zamzuri Hairi,
Hassan Nurhaffizah,
Wahid Nurbaiti
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5264
Subject(s) - acceleration , artificial neural network , tilt (camera) , correlation , head (geology) , curvature , computer science , simulation , motion (physics) , dynamics (music) , process (computing) , control theory (sociology) , artificial intelligence , engineering , mathematics , physics , acoustics , structural engineering , control (management) , geology , geometry , classical mechanics , geomorphology , operating system
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.