Prevalence and Level of Motion Sickness among the General Public of Mangaluru

Jacob, Jaison; Castelino, Patsey Sera

doi:10.5958/2349-2996.2017.00113.6

Cited by 1 publication

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“…MS is a disorder in which the visual perception of movement differs from the vestibular system's sense of direction. When exposed to intensive motion stimuli, nearly everyone experiences motion sickness [11]. The main symptoms and signs of MS are nausea and vomiting [12] as well as sweating, stomach awareness, facial pallor, and drowsiness (often referred to as "cold sweating" and "sopite syndrome" respectively), warm sensation in the body, dizziness, and usually losing appetite, and headache [13], [14].…”

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confidence: 99%

Comparison of Transfer Function Models to Represent the Correlation Between Vehicle Lateral Acceleration and Head Tilting Angle in Motion Sickness

Ali

Saruchi²

2022

Int. J. Automot. Mech. Eng.

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Motion Sickness (MS) is described as an unpleasant feeling caused by a forceful movement; hence vehicle movement impacts the severity of MS. While negotiating a curve, drivers and passenger tilt their heads differently, affecting their motion sickness incidence (MSI), which is the severity of MS. MS is a negative feeling, that affects occupant’s comfort, and to further understand the correlation between occupants' behavior and vehicle movement in MS and then represent it using mathematical models, it was proven that MSI could be predicted through mathematical models. However, there is an indefinite value between values between occupant’s behavior and vehicle movement. Based on that it is vital to express it the correlation mathematically. An experiment adopted from a prior study was utilized to get the data and develope the mathematical models with different proportions to represent the correlation between vehicle movement and occupant behavior in motion sickness in transfer function equations using system identification (SI), by utilising black-box feature to use the experimental data as input and output to allow SI to predict the transfer function models. The aim of this study is to investigate MS factors in relation to the vehicle movement and occupant’s behavior, to develop multiple transfer function models, to analyze and compare them. The results were obtained in the three different transfer function orders, second, third and fourth order functions for each proportion used for both the driver and passenger, the driver models’ results were in between 64.68%-67.87%, and the passenger results were in between 63.75%-67.93%, after the comparison the highest fits for each order were obtained. The highest fits amongst driver models were 67.87% (4th Order), 66.78% (3rd Order) and 65.17% (2nd Order) and 67.93% (4th Order), 66.3% (3rd Order) and 64.82% (2nd Order) amongst the passenger models. Those fits were then validated via Simulink with unseen data that was not used in identification process, and lastly the models Root Mean Square Error (RMSE) was obtained for all of them to determine their efficiency.

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