Analysis of female drivers’ ECG characteristics within the context of connected vehicles

Wang, Xiaoyuan; Guo, Yongqing; Chen, Chen; Xia, Yuanyuan; Liu, Yaqi

doi:10.1108/jicv-01-2019-0002

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…The number of outliers is extremely small, and individual outliers do not affect the results. Therefore, detailed analysis of outliers was not performed [ 35 , 47 ]. The maximum, minimum, median, average, and upper/lower quartiles of AVNN increased by the increase in degree of fatigue.…”

Section: Resultsmentioning

confidence: 99%

“…Wavelet threshold denoising can remove white noise [ 34 ]. We chose Daubechies-4 as the wavelet generating function, and the threshold selection is shown in Equation (1) [ 35 ]. After the ECG signal is decomposed by the wavelet threshold denoising, the wavelet coefficient of the effective signal is larger and that of the white noise is smaller [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

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Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Pan

Wang

et al. 2021

Sensors

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Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Pan

Wang

et al. 2021

Sensors

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show abstract

“…It was seen that after noise reduction, the noise can be controlled to an acceptable level. For more details about the ECG signal preprocessing, please refer to another article by Wang et al [36].…”

Section: Ecg Signal Datamentioning

confidence: 99%

Analysis of Differences in ECG Characteristics for Different Types of Drivers under Anxiety

Guo

Wang

et al. 2021

Advances in Civil Engineering

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Anxiety is a complex emotion characterized by an unpleasant feeling of tension when people anticipate a threat or negative consequence. It is regarded as a comprehensive reflection of human thought processes, physiological arousal, and external stimuli. The actual state of emotion can be represented objectively by human physiological signals. This study aims to analyze the differences of ECG (electrocardiogram) characteristics for various types of drivers under anxiety. We used several methods to induce drivers’ mood states (calm and anxiety) and then conducted the real and virtual driving experiments to collect driver’s ECG signal data. Physiological changes in ECG during the experiments were recorded using the PSYLAB software. The independent sample t-test analysis was conducted to determine if there are significant differences in ECG characteristics for different types of drivers in anxious state during driving. The results show that there are significant differences in ECG signal characteristics of drivers by gender, age, and driving experience, in time domain, frequency domain, and waveform under anxiety. Our findings of this study contribute to the development of more intelligent and personalized driver warning system, which could improve road traffic safety.

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“…Emotion Recognition Systems (ERS) can be created through various means, including analyzing facial expressions [3][4], speech patterns [5] [6], and physiological responses [7] [8]. Examples of physiological signals that have been used in empirical research to develop ERS include electroencephalogram (EEG) [9][10], temperature (TEMP) [11] [12], electrocardiogram (ECG) [13] [14], galvanic skin reaction (GSR) [15] [16], and photoplethysmography (PPG) [17] [18].…”

Section: Introductionmentioning

confidence: 99%

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

Gupta

Sharma

Bhardwaj

et al. 2023

Preprint

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In this study, a hybrid architecture combining a Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU) is proposed for multi-class emotion recognition using EEG signals.Emotion recognition using EEG signals is a challenging task due to the ever-changing nature of EEG signals and the high dimensionality of the feature space. The proposed approach aims to address these challenges by utilizing a hybrid architecture that combines the strengths of both 1D-CNN and GRU. The 1D-CNN is used to retrieve relevant spatial features from the EEG signals, while the GRU is employed to capture the temporal dependencies in the signals. The models were used to classify multi-class emotions: four and sixteen emotions based on the valence-arousal and valence-arousal-liking-dominance planes, respectively, using the benchmark DEAP dataset. The experiment results showed that the proposed models achieved high accuracy in classifying emotions for both four and sixteen emotions as compared to state of art methods. The results of this research have significant implications for the development of affective computing systems in various fields, including healthcare, human-computer interaction, and education. In conclusion, this study demonstrates the potential of deep learning models in affective computing and provides a foundation for future research in this field. The use of reliable physiological signals and the combination of different architectures have shown to be effective in accurately classifying emotions.

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Analysis of female drivers’ ECG characteristics within the context of connected vehicles

Cited by 8 publications

References 31 publications

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Analysis of Differences in ECG Characteristics for Different Types of Drivers under Anxiety

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

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