Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.

Surface electrogastrography (EGG) is a non-invasive technique that is used to record myoelectrical activity of the stomach using cutaneous electrodes placed on the abdomen. Gastric motility assessment by EGG is a candidate for standard clinical procedure based on the quantification of parameters characteristic of gastric motility disorders. Despite its noticeable benefits, EGG is not widely implemented in clinical practice. The main reasons are: (1) lack of standardization of electrode placement, (2) time-consuming diagnostic procedures and (3) a complex multi-channel recording setup. We proposed a methodology in which an easy-to-use single-channel EGG, with a less time-consuming protocol (<1 h), would provide sufficient information for gastric motility assessment. Recordings from the three anatomical landmarks in 20 healthy young subjects were compared under two conditions, fasting and postprandial by evaluating the dominant frequency (DF). Our results showed that there is a statistically significant increase of DF after meal ingestion (p<0.05) in each of the three channels. However, when the study group was divided into two subgroups based on body mass index (BMI), the most appropriate recording location was above the body of the stomach (according to statistical significance p=7.82×10⁻⁶). We showed that a less time-consuming recording session with light meal intake could be used for the assessment of gastric myoelectrical activity (GMA).

In the era of technological advances and innovations in transportation technologies, application of driving simulators for the investigation and assessment of the driving process provides a safe and suitable testing environment. Although driving simulators are crucial for further improvements in transportation, it is important to resolve one of their main disadvantages–simulator sickness. Therefore, suitable methods for the assessment of simulator sickness are required. The main aim of this paper was to present a non-invasive method for assessing simulator sickness by recording gastric myoelectrical activity–electrogastrography. Open-source hardware for electrogastrography together with recordings obtained in 13 healthy volunteers is presented, and the main aspects of signal processing for artifact cancellation and feature extraction were discussed. Based on the obtained results, it was concluded that slow-wave electrical gastric activity can be recorded during driving simulation by following adequate recommendations and that proposed features could be beneficial in describing non-ordinary electrogastrography signals.

Electrogastrography (EGG) is a method for recording electrical potentials originating from the smooth stomach muscles. Though normal gastric rhythm at the first glance appears relatively simple in nature resembling sinusoidal shape, the main drawbacks for wider application of EGG technique are low frequencies (dominant frequency of normal gastric rhythm is around 0.05 Hz) and low amplitudes (approximately in range from 0.1 mV to 0.5 mV) making it vulnerable to noises, especially to the motion artifacts. This chapter introduces practical considerations for EGG signal processing for simulator sickness assessment with fundamentals of the signal origin and the recording methodology. The landscape of EGG analysis based on the existing body of knowledge reveals EGG potential and urges on EGG standardization required for reusability and replicability of EGG signal processing techniques. The processing workflows in static and dynamic conditions with some practical considerations are proposed. Finally, the chapter tackles possible directions towards development of novel methods for noise elimination and feature extraction for EGG-based simulator sickness assessment. <!----> <!----> <!----> <!---->

The comparison of two methods used to separate electrocardiography and electromyography signals: Empirical Mode Decomposition (EMD) and Fractional Order Calculus (FC) is presented. For quantitative evaluation, crest factor (CF) and signal-to-noise ratio (SNR) values were calculated, where higher CF and SNR values were preferable. Results suggest that FC and EMD performance can be partially assessed by CF indicating better artifact cancellation with EMD approach by 9.72%. For FC and EMD, automatic fractional order application and intrinsic mode function selection were suggested and discussed, respectively. In summary, the results suggested that proposed filtering assessment should be enhanced for more appropriate FC and EMD method's assessment.