Fatigue driving is one of the main reasons for the occurrence of traffic accidents. Brain-computer interface, as a human-computer interaction method based on EEG signals, can communicate with the outside world and move freely through brain signals without relying on the peripheral neuromuscular system. In this paper, a simulation driving platform composed of driving simulation equipment and driving simulation software is used to simulate the real driving process. The EEG signals of the subjects are collected through simulated driving, and the EEG of five subjects is selected as the training sample, and the remaining one is the subject. As a test sample, perform feature extraction and classification experiments, select any set of normal signals and fatigue signals recorded in the driving fatigue experiment for data analysis, and then study the classification of driver fatigue levels. Experiments have proved that the PSO-H-ELM algorithm has only about 4% advantage compared with the average accuracy of the KNN algorithm and the SVM algorithm. The gap is not as big as expected, but as a new algorithm, it is applied to the detection of fatigue EEG. The two traditional algorithms are indeed more suitable. It shows that the driver fatigue level can be judged by detecting EEG, which will provide a basis for the development of on-board, real-time driving fatigue alarm devices. It will lay the foundation for traffic management departments to intervene in driving fatigue reasonably and provide a reliable basis for minimizing traffic accidents.
With the increasing number of motor vehicles globally, the casualties and property losses caused by traffic accidents are substantial worldwide. Traffic accidents caused by fatigue driving are also increasing year by year. In this article, the authors propose a functional brain network‐based driving fatigue detection method and seek to combine features and algorithms with optimal effect. First, a simulated driving experiment is established to obtain EEG signal data from multiple subjects in a long‐term monotonic cognitive task. Second, the correlation between each EEG signal channel is calculated using Pearson correlation coefficient to construct a functional brain network. Then, five functional brain network features (clustering coefficient, node degree, eccentricity, local efficiency, and characteristic path length) are extracted and combined to obtain a total of 26 features and eight machine learning algorithms (SVM, LR, DT, RF, KNN, LDA, ADB, GBM) are used as classifiers for fatigue detection respectively. Finally, the optimal combination of features and algorithms are obtained. The results show that the feature combination of node degree, local efficiency, and characteristic path length achieves the best classification accuracy of 92.92% in the logistic regression algorithm.
The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.
At present, the global positioning system has been widely used in outdoor positioning and navigation, but in buildings, thick walls made of cement and bricks have a certain blocking effect on satellite signals, resulting in severely weakened GPS signals, and also extremely it may have an impact on the positioning accuracy, so that the positioning task cannot be completed. Inertial sensor is a kind of sensor mainly detecting and measuring acceleration tilt impact vibration rotation and multi-degree-of-freedom motion, is an important part to solve navigation orientation and motion carrier control. Therefore, we need to study the positioning system and positioning method of the people in the building according to the indoor positioning technology. This article mainly uses inertial sensors as indoor positioning technology for related research. This technology mainly includes linked inertial navigation algorithm and pedestrian dead reckoning algorithm. This paper uses pedestrian dead reckoning algorithm to study the positioning system. The research shows that the PDR algorithm designed this time is more accurate for the positioning of pedestrian movement in the scene of the building. Compared with the previous algorithm research, it has increased by 1%-2% accuracy meets the expected accuracy requirements.
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