Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator.
Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator.
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DOI：
10.3390/s22228616
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发表时间：
2022-11-08
期刊：
影响因子：
--
通讯作者：
Miljković N
中科院分区：
文献类型：
--
作者：
Jakus G;Sodnik J;Miljković N
用于自动驾驶体验评估的驾驶模拟器的快速发展受到模拟器疾病相关恶心的限制。胃电图（EGG）为基础的方法可能是有前途的即时，客观和定量的恶心评估。鉴于相对较高的EGG灵敏度与相对较低的振幅和频率跨度相关的噪声，我们介绍了一个自动化的程序，包括统计分析和机器学习技术，用于基于EGG的恶心检测与自动驾驶模拟过程中的噪声污染。我们计算了EGG振幅的均方根，中值和主频率，功率谱密度（PSD）在主频率的幅度，PSD的波峰因数，以及频谱变化分布沿着与新引入的参数：样本和频谱熵，自相关零交叉，和来自连续EGG样本的庞加莱图的参数。结果表明，样本熵的鲁棒性突出，自相关零交叉，主频，其中位数具有中等的鲁棒性。机器学习达到了88.2%的准确率，并显示样本熵是最相关和最强大的参数之一，而线性分析突出了光谱熵，光谱变化分布和PSD的波峰因子。这项研究清楚地表明，需要在嘈杂的环境中进行定制的特征选择，以及一种包括机器学习和统计分析的补充方法，以实现有效的恶心检测。
The rapid development of driving simulators for the evaluation of automated driving experience is constrained by the simulator sickness-related nausea. The electrogastrogram (EGG)-based approach may be promising for immediate, objective, and quantitative nausea assessment. Given the relatively high EGG sensitivity to noises associated with the relatively low amplitude and frequency spans, we introduce an automated procedure comprising statistical analysis and machine learning techniques for EGG-based nausea detection in relation to the noise contamination during automated driving simulation. We calculate the root mean square of EGG amplitude, median and dominant frequencies, magnitude of Power Spectral Density (PSD) at dominant frequency, crest factor of PSD, and spectral variation distribution along with newly introduced parameters: sample and spectral entropy, autocorrelation zero-crossing, and parameters derived from the Poincaré diagram of consecutive EGG samples. Results showed outstanding robustness of sample entropy with moderate robustness of autocorrelation zero-crossing, dominant frequency, and its median. Machine learning reached an accuracy of 88.2% and revealed sample entropy as one of the most relevant and robust parameters, while linear analysis highlighted spectral entropy, spectral variation distribution, and crest factor of PSD. This study clearly indicates the need for customized feature selection in noisy environments, as well as a complementary approach comprising machine learning and statistical analysis for efficient nausea detection.
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影响因子：
3.3
作者：
Arlot, Sylvain;Celisse, Alain
通讯作者：
Celisse, Alain
DOI：
10.1016/b978-0-444-63437-5.00027-3
发表时间：
2016-01-01
期刊：
NEURO-OTOLOGY
影响因子：
--
作者：
Golding, J. F.
通讯作者：
Golding, J. F.
影响因子：
4.6
作者：
Delgado-Bonal, Alfonso
通讯作者：
Delgado-Bonal, Alfonso
DOI：
10.1111/anec.12919
发表时间：
2022-01
期刊：
Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
影响因子：
--
作者：
Boljanić T;Miljković N;Lazarevic LB;Knezevic G;Milašinović G
通讯作者：
Milašinović G
DOI：
10.1177/0954407013516101
发表时间：
2014-06-01
影响因子：
1.7
作者：
Aykent, Baris;Merienne, Frederic;Kemeny, Andras
通讯作者：
Kemeny, Andras