信号处理方法在疲劳驾驶和亚健康研究中的应用
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摘要
随着社会的进步与发展,由疲劳引发的各种问题层出不穷,严重影响了人们正常的学习、工作和生活。其中尤以疲劳驾驶和亚健康的危害性巨大,已经成为威胁人类生命安全的“杀手”之一。因此,科学、合理、客观地对疲劳驾驶和亚健康进行检测并为相关部门提供干预依据具有非常重要的现实意义。
生理信号中蕴含了丰富的与人体生理活动、精神状态以及疾病诊断密切相关的信息。因此,可以通过分析生理信号来实现疲劳驾驶和亚健康的检测。人体的生理信号分析是信号处理技术的典型应用之一,它能够客观、有效地提取生理信号的内在特征与本质信息。本文在现有研究的基础上,从生理信号的角度出发,采用现代信号处理技术对疲劳驾驶和亚健康状态的检测进行研究,采集了12名受试者模拟驾驶的脑电信号以及30名受试者的脉搏信号分析探索能够反映疲劳驾驶与亚健康状态的有效特征。本文的主要工作如下：
首先,分别采用希尔伯特-黄变换和小波变换对待分析的脑电信号和脉搏信号进行消噪处理,以此提高分析结果的准确性。通过对比消噪前后信号的波形图,发现希尔伯特-黄变换和小波变换可以有效的去除脑电信号和脉搏信号中夹杂的噪声干扰信号,从而为进一步的信号分析奠定基础。
其次,分别使用相对功率谱、Wigner-Ville分布以及功率谱信息熵对驾驶员在不同驾驶时刻的脑电信号进行分析,发现不同驾驶时刻脑电信号各节律的相对功率谱和功率谱的信息熵值存在明显的差异,并且脑电信号的Wigner-Ville分布的时频谱在不同驾驶时刻下均不相同,从而表明相对功率潜、Wigner-Ville分布以及功率谱信息熵可用于疲劳驾驶的检测。
最后,使用匹配追踪算法和Gabor变换分别对健康受试人和亚健康受试人的脉搏信号进行特征提取,并对其进行了分析与比较,再将选取的特征作为输入向量通过K-近邻分类器对健康和亚健康状态进行分类识别,取得了良好的分类效果,从而表明匹配追踪算法和Gabor变换可用于人体亚健康状态的检测。
综上所述,相对功率谱、Wigner-Ville分布以及功率谱信息熵均可用于疲劳驾驶时脑电信号的特征分析,并能有效的对驾驶员的疲劳状态进行判定。匹配追踪算法和Gabor变换可用于提取人体亚健康状态的脉搏信号的特征,并能取得良好的分类效果,可作为亚健康检测的参考指标。由此可见,现代信号处理方法可以有效地提取表征疲劳驾驶与亚健康状态的特征,从而为疲劳驾驶的检测和亚健康的诊断开辟一条新的有效途径。
Along with the social progress and development, the problems caused by fatigue have emerged, affecting seriously the people's normal study, work and life. Especially the harmfulness of fatigue driving and sub-health are enormous, they have been one of the "killer" of people's life. Therefore, the scientific, rational, objectively detect fatigue driving and sub-health and provide interference basis for relevant department have very important practical significance.
Physiological signal contains massive information which closely related human body's physiological activity, mental state and disease diagnosis. Therefore, detection of fatigue driving and sub-health can be achieved by analysis physiological signal. The analysis of the body's physiological signal is one of the typical applications of signal processing technology. It can effectively extract the intrinsic characteristics and essential information of the physiological signal. Based on the existed theories, this article uses the modern signal processing technology to study fatigue driving and sub-health from the point of view of physiological signal. And it specific collects the EEG signals from12subjects in the process of a driving simulation and the pulse signals from30subjects to detect fatigue driving and sub-health state, and then explore the effective characteristics of driver fatigue and sub-health state. The main work of this study is:
Firstly, EEG signals and pulse signals are de-noised respectively through Hilbert-Huang transform and Wavelet transform, in order to improve the accuracy of the analysis. By comparison with signal waveform before de-noising and signal waveform after de-noising, found that Hilbert-Huang transform and Wavelet transform can effectively remove the interference signal which be mingled with the EEG signal and pulse signal, so as to lay a foundation for further signal analysis.
Secondly, relative power spectrum, Wigner-Ville distribution and power spectrum information entropy are respectively used to analyze the EEG signals at different driving times, finding that EEG rhythms' relative power spectrum and spectrum information entropy values have existed obvious differences at different driving times, and the Wigner-Ville distributions of EEG are also not the same at different times, which demonstrates the relative power spectrum, Wigner-Ville distribution and power spectrum information entropy can be used for detecting fatigue driving.
Finally, Matching pursuit algorithm and Gabor transform are adopted to extract pulse signals' features of the health and sub-health, and have carried on the analysis and comparison, then the health and sub-health state are classified by K-nearest neighbors classifier based on the selected feature as the input vector, and achieves good classification effect, which indicates that the Matching pursuit algorithm and Gabor transform can be used for detecting sub-health state.
In sum, the relative power spectrum, Wigner-Ville distribution and power spectrum information entropy can be used to analyze EEG feature in the process of driving, with a result whether a driver is at fatigue state. The Matching pursuit algorithm and Gabor transform can be used to extract the pulse signal features of sub-health and can obtain good classification effect, thus they might be considered as reference index of sub-health detection. Thus, it can be seen that signal processing methods can effectively extract the characterization of fatigue driving and sub-health state, and open up a practical way for the driver fatigue detection and diagnosis of sub-health.
生理信号中蕴含了丰富的与人体生理活动、精神状态以及疾病诊断密切相关的信息。因此,可以通过分析生理信号来实现疲劳驾驶和亚健康的检测。人体的生理信号分析是信号处理技术的典型应用之一,它能够客观、有效地提取生理信号的内在特征与本质信息。本文在现有研究的基础上,从生理信号的角度出发,采用现代信号处理技术对疲劳驾驶和亚健康状态的检测进行研究,采集了12名受试者模拟驾驶的脑电信号以及30名受试者的脉搏信号分析探索能够反映疲劳驾驶与亚健康状态的有效特征。本文的主要工作如下：
首先,分别采用希尔伯特-黄变换和小波变换对待分析的脑电信号和脉搏信号进行消噪处理,以此提高分析结果的准确性。通过对比消噪前后信号的波形图,发现希尔伯特-黄变换和小波变换可以有效的去除脑电信号和脉搏信号中夹杂的噪声干扰信号,从而为进一步的信号分析奠定基础。
其次,分别使用相对功率谱、Wigner-Ville分布以及功率谱信息熵对驾驶员在不同驾驶时刻的脑电信号进行分析,发现不同驾驶时刻脑电信号各节律的相对功率谱和功率谱的信息熵值存在明显的差异,并且脑电信号的Wigner-Ville分布的时频谱在不同驾驶时刻下均不相同,从而表明相对功率潜、Wigner-Ville分布以及功率谱信息熵可用于疲劳驾驶的检测。
最后,使用匹配追踪算法和Gabor变换分别对健康受试人和亚健康受试人的脉搏信号进行特征提取,并对其进行了分析与比较,再将选取的特征作为输入向量通过K-近邻分类器对健康和亚健康状态进行分类识别,取得了良好的分类效果,从而表明匹配追踪算法和Gabor变换可用于人体亚健康状态的检测。
综上所述,相对功率谱、Wigner-Ville分布以及功率谱信息熵均可用于疲劳驾驶时脑电信号的特征分析,并能有效的对驾驶员的疲劳状态进行判定。匹配追踪算法和Gabor变换可用于提取人体亚健康状态的脉搏信号的特征,并能取得良好的分类效果,可作为亚健康检测的参考指标。由此可见,现代信号处理方法可以有效地提取表征疲劳驾驶与亚健康状态的特征,从而为疲劳驾驶的检测和亚健康的诊断开辟一条新的有效途径。
Along with the social progress and development, the problems caused by fatigue have emerged, affecting seriously the people's normal study, work and life. Especially the harmfulness of fatigue driving and sub-health are enormous, they have been one of the "killer" of people's life. Therefore, the scientific, rational, objectively detect fatigue driving and sub-health and provide interference basis for relevant department have very important practical significance.
Physiological signal contains massive information which closely related human body's physiological activity, mental state and disease diagnosis. Therefore, detection of fatigue driving and sub-health can be achieved by analysis physiological signal. The analysis of the body's physiological signal is one of the typical applications of signal processing technology. It can effectively extract the intrinsic characteristics and essential information of the physiological signal. Based on the existed theories, this article uses the modern signal processing technology to study fatigue driving and sub-health from the point of view of physiological signal. And it specific collects the EEG signals from12subjects in the process of a driving simulation and the pulse signals from30subjects to detect fatigue driving and sub-health state, and then explore the effective characteristics of driver fatigue and sub-health state. The main work of this study is:
Firstly, EEG signals and pulse signals are de-noised respectively through Hilbert-Huang transform and Wavelet transform, in order to improve the accuracy of the analysis. By comparison with signal waveform before de-noising and signal waveform after de-noising, found that Hilbert-Huang transform and Wavelet transform can effectively remove the interference signal which be mingled with the EEG signal and pulse signal, so as to lay a foundation for further signal analysis.
Secondly, relative power spectrum, Wigner-Ville distribution and power spectrum information entropy are respectively used to analyze the EEG signals at different driving times, finding that EEG rhythms' relative power spectrum and spectrum information entropy values have existed obvious differences at different driving times, and the Wigner-Ville distributions of EEG are also not the same at different times, which demonstrates the relative power spectrum, Wigner-Ville distribution and power spectrum information entropy can be used for detecting fatigue driving.
Finally, Matching pursuit algorithm and Gabor transform are adopted to extract pulse signals' features of the health and sub-health, and have carried on the analysis and comparison, then the health and sub-health state are classified by K-nearest neighbors classifier based on the selected feature as the input vector, and achieves good classification effect, which indicates that the Matching pursuit algorithm and Gabor transform can be used for detecting sub-health state.
In sum, the relative power spectrum, Wigner-Ville distribution and power spectrum information entropy can be used to analyze EEG feature in the process of driving, with a result whether a driver is at fatigue state. The Matching pursuit algorithm and Gabor transform can be used to extract the pulse signal features of sub-health and can obtain good classification effect, thus they might be considered as reference index of sub-health detection. Thus, it can be seen that signal processing methods can effectively extract the characterization of fatigue driving and sub-health state, and open up a practical way for the driver fatigue detection and diagnosis of sub-health.
引文
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