Measuring driving simulator adaptation using EDA

Abstract

Most research about simulator adaptation focus on driving style and participants' comfort. However, in recent years, there is a growing interest in physiological data analysis as part of the user experience (UX) assessment. Furthermore, the application of machine learning (ML) techniques to those data may allow the automatic detection of stress and cognitive load. Previously, we noticed that new participants in experiments with our simulator were often in a constant state of tension. This prevented optimal training of our ML models as many of the collected data were not representative of a person's normal state.Our work focuses on improving driver's UX by keeping the cognitive load and stress at levels that do not interfere with the primary task of driving. We use a custom-made driving simulator as our testing platform and evaluate participants' emotional state with physiological signals, specifically electrodermal activity (EDA). EDA is the variation of the skin conductance created by sweat glands. It is linked to the sympathetic nervous system and is an indication of physiological and psychological arousal. We selected EDA because several studies have shown that it is a fast indicator of stress and cognitive load.To ensure that we are consistently collecting accurate data that could be fed to ML algorithms, we need to be able to correlate physiological reactions to external stimuli. We want to avoid them to be confused with general tension. Therefore, we need to determine the time it takes for most participants to physiologically adapt to our simulator. In this between-subjects study, we examined the impact of short time (ca. 10 min) exposures to the simulation and compared it with a longer exposure period (ca. 35 min).Another problem we faced was that some participants were too indisposed by driving in the simulator to complete testing sessions. Therefore, we needed to find a way to discriminate them during the recruitment process. Literature has shown that there might be a link between motion sickness and simulator sickness and in this study, we searched for a correlation between the motion sickness susceptibility questionnaire (MSSQ) and the self-reported simulator sickness using the simulator sickness questionnaire (SSQ).For our investigation, we recruited 22 people through an agency. They were divided in two groups. Group A (short-time exposures) had 10 participants between 25 and 69 years old (M=49.5; SD=17.1, 5 women, 5 men) and group B (long-time exposure) had 12 people between 28 and 65 years old (M=43; SD=12.8, 5 women, 7 men). We requested from the agency to recruit only active drivers of automatic transmissions cars as our simulator mimics this type of vehicle.Motion sickness susceptibility and discomfort felt in the simulator are moderately correlated. The coefficient value is 0.51. The number of participants of our study being small, further research is necessary to determine if the MSSQ can be used as a discriminator in the recruitment phase. In addition, we can conclude that a longer exposure of 35 min results overall in better physiological adaptation.

Keywords: User Experience, Driving Simulator, Stress, Cognitive Load, Machine Learning, EDA, Motion Sickness

DOI: 10.54941/ahfe1001489