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自主车辆中的胃电图-模拟驾驶环境中运动病评估的客观方法。

Electrogastrography in Autonomous Vehicles-An Objective Method for Assessment of Motion Sickness in Simulated Driving Environments.

机构信息

Faculty of Electrical Engineering, University of Ljubljana, Tržaška c. 25, 1000 Ljubljana, Slovenia.

School of Electrical Engineering, University of Belgrade, B. kralja Aleksandra 73, 11000 Belgrade, Serbia.

出版信息

Sensors (Basel). 2021 Jan 14;21(2):550. doi: 10.3390/s21020550.


DOI:10.3390/s21020550
PMID:33466805
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7830998/
Abstract

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.

摘要

自动驾驶车辆有望完全控制驾驶过程,使驾驶员只能充当乘客。这可能会导致更多的疾病,因为他们可以从事驾驶以外的其他任务。采用不同的疾病缓解技术,为我们研究自动驾驶车辆中的不同类型的晕动病提供了可能。在本文中,我们报告了一项研究,该研究探讨了使用胃电图(EGG)评估晕车的可能性,EGG 是一种非侵入性方法,用于测量胃的肌电活动,以及它在自动驾驶车辆(AV)中的潜在用途。该研究在具有虚拟现实(VR)头戴式设备的高保真度驾驶模拟器中进行。进行了单独的 EGG 测量:在 VR 中的自动驾驶 AV 模拟视频之前、期间和之后。在驾驶过程中,参与者遇到了两种驾驶环境:一条笔直且动态较小的高速公路和一条高度动态且弯曲的乡村道路。使用专有 3 通道记录设备和 Ag/AgCl 皮肤电极记录 EGG 信号。此外,参与者被要求通过按下特殊按钮来标记何时感到不适和恶心。驾驶后,他们还完成了模拟器疾病问卷(SSQ),并报告了他们对疾病症状的整体主观感知。EGG 结果显示,在 AV 模拟过程中,主导频率(DF)和高功率谱密度(FSD)的百分比显著增加,功率谱密度峰度因子(CF)显著降低。绝大多数参与者报告在更动态的条件下感到恶心,并伴有 EGG 幅度的增加和 RMS 值的增加。报告的恶心与 EGG 幅度的增加同时发生。基于这些结果,我们得出结论,EGG 可用于评估自动驾驶车辆中的晕车。DF、CF 和 FSD 可用作整体疾病指标,而 EGG 信号幅度的相对增加和增加的持续时间可用作短期疾病指标,其中驾驶环境可能会影响驾驶员。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/b5f13624ab1c/sensors-21-00550-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/dd902a5eaa12/sensors-21-00550-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/f7f1e1e58d1d/sensors-21-00550-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/27d8fd1dbdf6/sensors-21-00550-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/3566e2875fb2/sensors-21-00550-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/f9401712e874/sensors-21-00550-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/e292725ecdbb/sensors-21-00550-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/47a40a3913b3/sensors-21-00550-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/b5f13624ab1c/sensors-21-00550-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/dd902a5eaa12/sensors-21-00550-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/f7f1e1e58d1d/sensors-21-00550-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/27d8fd1dbdf6/sensors-21-00550-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/3566e2875fb2/sensors-21-00550-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/f9401712e874/sensors-21-00550-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/e292725ecdbb/sensors-21-00550-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/47a40a3913b3/sensors-21-00550-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db3/7830998/b5f13624ab1c/sensors-21-00550-g007.jpg

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[2]
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[3]
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[5]
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[8]
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[9]
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[10]
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引用本文的文献

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[2]
Quantitative study on objective indicators for assessing motion sickness susceptibility based on Vestibulo-Ocular Reflex experiments.

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[3]
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[4]
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[5]
Data augmentation for generating synthetic electrogastrogram time series.

Med Biol Eng Comput. 2024-9

[6]
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[7]
Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator.

Sensors (Basel). 2022-11-8

[8]
Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning.

Front Neurol. 2022-4-1

[9]
Relating individual motion sickness levels to subjective discomfort ratings.

Exp Brain Res. 2022-4

本文引用的文献

[1]
Efficacy of augmented visual environments for reducing sickness in autonomous vehicles.

Appl Ergon. 2021-1

[2]
Moving base driving simulators' potential for carsickness research.

Appl Ergon. 2019-7-18

[3]
Lessons Learned: Gastric Motility Assessment During Driving Simulation.

Sensors (Basel). 2019-7-19

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Simple gastric motility assessment method with a single-channel electrogastrogram.

Biomed Tech (Berl). 2019-4-24

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Torso-Tank Validation of High-Resolution Electrogastrography (EGG): Forward Modelling, Methodology and Results.

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Appl Ergon. 2016-3

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