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基于生理特征的道路催眠识别方法。

A Recognition Method for Road Hypnosis Based on Physiological Characteristics.

机构信息

College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China.

Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong, Qingdao 266000, China.

出版信息

Sensors (Basel). 2023 Mar 23;23(7):3404. doi: 10.3390/s23073404.

DOI:10.3390/s23073404
PMID:37050464
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10099380/
Abstract

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.

摘要

道路催眠是一种在单调场景中容易频繁出现的状态，对交通安全有很大影响。有效的道路催眠检测可以提高智能车辆的性能。本文设计并开展了模拟实验和车辆实验，以获得道路催眠的生理特征数据。提出了一种基于生理特征的道路催眠识别模型。采用高阶谱对心电图（ECG）和肌电图（EMG）数据进行预处理，再通过主成分分析（PCA）进行融合。构建了线性判别分析（LDA）、二次判别分析（QDA）和 K 近邻（KNN）模型来识别道路催眠。该模型对道路催眠具有良好的识别性能，为实时、准确识别道路催眠提供了更多的选择方法和技术支持，对提高智能车辆的智能化和主动安全性具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/2b22b0ceeb40/sensors-23-03404-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/eb99c3ec0993/sensors-23-03404-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/0bf7e76f0f24/sensors-23-03404-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/2b22b0ceeb40/sensors-23-03404-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/a1e30d188028/sensors-23-03404-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/5a98dee4148c/sensors-23-03404-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/35ea8d0ccbf8/sensors-23-03404-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/a13ee264266d/sensors-23-03404-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/6a90ec13397a/sensors-23-03404-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/d5708f0f2d9f/sensors-23-03404-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/eb99c3ec0993/sensors-23-03404-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb74/10099380/0bf7e76f0f24/sensors-23-03404-g008.jpg
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A real-time driver fatigue identification method based on GA-GRNN.基于 GA-GRNN 的实时驾驶员疲劳识别方法。
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