CN108903958A - Driver attention evaluates early warning system and its implementation method - Google Patents
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Abstract
本发明揭示了一种单点采集的司机注意力评价及预警解决方案,实时监测司机的驾驶状态,以便当司机处于疲劳状态时予以可靠提醒。其主要包括脑电采集子系统、注意力评价子系统以及驾驶预警子系统。采用脑波耳机实时记录来自司机前额的EEG信号,由蓝牙传输到注意力评价子系统予以分析;注意力评价子系统利用开始1分钟内采集到的θ、α节律波的数据构建各自的样本空间。将司机的驾驶疲劳状态分类并将分析结果传输到驾驶预警子系统,司机的智能手机对分析结果进行显示并自动实施相应地预警操作。应用本发明的司机注意力评价预警方案,设备结构简单、便于携带穿戴、预警及时,同时降低了算法复杂度,能降低因疲劳驾驶导致的交通事故率。
The invention discloses a driver attention evaluation and early warning solution for single-point collection, which monitors the driver's driving state in real time so as to give reliable reminders when the driver is in a fatigued state. It mainly includes EEG acquisition subsystem, attention evaluation subsystem and driving warning subsystem. Brainwave earphones are used to record the EEG signals from the driver's forehead in real time, and the signals are transmitted to the attention evaluation subsystem via Bluetooth for analysis; the attention evaluation subsystem uses the data of theta and alpha rhythm waves collected in the first 1 minute to construct their respective sample spaces . The driver's driving fatigue status is classified and the analysis results are transmitted to the driving warning subsystem. The driver's smartphone displays the analysis results and automatically implements corresponding warning operations. Applying the driver attention evaluation and early warning scheme of the present invention, the device has a simple structure, is easy to carry and wear, and has timely early warning, while reducing algorithm complexity and reducing the traffic accident rate caused by fatigue driving.
Description
技术领域technical field
本发明涉及一种基于脑电信号分析的疲劳驾驶预警系统,属于机电与通信的结合领域。The invention relates to a fatigue driving early warning system based on electroencephalogram signal analysis, which belongs to the field of combination of electromechanical and communication.
背景技术Background technique
调查研究显示,疲劳是影响司机安全驾驶的一个重要因素。司机疲劳时,会出现视线模糊、腰酸背疼、动作呆板、手脚发胀或有精力不集中、反应迟钝、思考不周全、精神涣散、焦虑、急躁等现象。如果仍勉强驾驶车辆,则很可能导致交通事故的发生。据不完全统计全世界每年发生的交通事故高达10多亿次,而由于司机的疲劳驾驶引起的事故约占总事故件数的20% ~30%,且疲劳驾驶造成的事故死亡率占所有交通事故死亡率的70%左右。故近年来,司机疲劳驾驶问题已受到世界各国越来越多研究人员的关注,其中针对疲劳驾驶检测方法而进行的研究更具重要的现实意义。为了控制机动车驾驶事故,疲劳驾驶的解决成为了重中之重。Research studies have shown that fatigue is an important factor affecting drivers' safe driving. When the driver is tired, there will be blurred vision, sore back pain, dull movements, swollen hands and feet, or lack of concentration, slow response, incomplete thinking, lack of energy, anxiety, and impatience. If you still drive the vehicle reluctantly, it is likely to cause a traffic accident. According to incomplete statistics, there are more than 1 billion traffic accidents in the world every year, and the accidents caused by driver fatigue account for about 20% to 30% of the total number of accidents, and the fatality rate of accidents caused by fatigue driving accounts for 20% of all traffic accidents. About 70% of the mortality rate. Therefore, in recent years, the problem of driver fatigue driving has attracted the attention of more and more researchers from all over the world, and the research on fatigue driving detection methods has more important practical significance. In order to control motor vehicle driving accidents, the solution to fatigue driving has become a top priority.
近年来,随着脑机接口(BCI)领域技术的飞速发展,使得仅仅通过人脑的思维活动实现对外部环境的控制成为可能。大量文献指出,脑电信号(英文简称EEG)各节律波(δ波、θ波、α波、β波)与人脑所处的诸如警觉、疲劳等状态具有紧密的联系。当人体处于警觉状态时,脑电信号中的α波在前额区的活动明显强于其他波段;而当人体处于疲劳状态时,α波将受到抑制,而θ波在颞叶区域的活动变得最为显著。因此,利用脑电信号节律波的变化情况来判断驾驶者所处状态已成为可能。In recent years, with the rapid development of brain-computer interface (BCI) technology, it has become possible to control the external environment only through the thinking activities of the human brain. A large number of literatures point out that the rhythmic waves (δ wave, θ wave, α wave, β wave) of the electroencephalogram signal (EEG for short) are closely related to the states of the human brain such as alertness and fatigue. When the human body is in a state of alertness, the activity of the α wave in the EEG signal in the frontal area is significantly stronger than that of other wave bands; and when the human body is in a state of fatigue, the α wave will be suppressed, while the activity of the θ wave in the temporal lobe area becomes most notably. Therefore, it has become possible to judge the state of the driver by utilizing the changes in the rhythm wave of the EEG signal.
发明内容Contents of the invention
为了解决上述技术问题,本发明的目的旨在提出一种单点采集的司机注意力评价及预警方案,基于脑电信号的注意力分析来检测驾驶员的疲劳程度,解决当司机感到疲劳时系统予以自动及时提醒的问题。In order to solve the above-mentioned technical problems, the object of the present invention is to propose a single-point acquisition driver's attention evaluation and early warning scheme, to detect the driver's fatigue degree based on the attention analysis of EEG signals, and to solve the problem of the driver's fatigue when the driver feels tired. Issues that are automatically and promptly reminded.
本发明实现上述目的的一种技术解决方案是,司机注意力评价预警系统,与司机的智能手机相关联,其特征在于系统构成包括:A kind of technical solution that the present invention realizes above-mentioned purpose is, the driver's attention evaluation early warning system is associated with the driver's smart phone, and it is characterized in that the system composition comprises:
脑电采集子系统:用于实时记录驾驶状态中司机的EEG信号,并信号处理作为注意力评价的源数据;EEG acquisition subsystem: used for real-time recording of the EEG signal of the driver in the driving state, and signal processing as the source data of attention evaluation;
注意力评价子系统:用于对源数据进行滤波、提取所需波段的时域波形并进行算法分析和分类识别,得到疲劳状态;Attention evaluation subsystem: used to filter the source data, extract the time-domain waveform of the required band, and perform algorithm analysis and classification identification to obtain the fatigue state;
驾驶预警子系统:为基于智能手机硬件的APP,用于根据所得到的司机疲劳状态,并结合设定的驾驶模式通过智能手机反馈对应注意力弱集中状态的预警信息;Driving warning subsystem: it is an APP based on smart phone hardware, which is used to feed back early warning information corresponding to the state of weak concentration through the smart phone according to the obtained driver's fatigue state and combined with the set driving mode;
三个子系统逐次通信相联且数据交互。The three subsystems communicate and communicate with each other successively and exchange data.
进一步地,所述脑电采集子系统面向注意力评价子系统单向通信,所述注意力评价子系统面向驾驶预警子系统单向通信,且单向通信基于蓝牙设备实现。Further, the EEG acquisition subsystem is oriented to the one-way communication of the attention evaluation subsystem, and the attention evaluation subsystem is oriented to the one-way communication of the driving warning subsystem, and the one-way communication is realized based on a Bluetooth device.
进一步地,所述脑电采集子系统为针对EEG信号在正常范围内的波动趋势和个体差异动态补偿的单通道采集器,采集点设为司机的侧向前额、而参考点设为司机的一侧耳垂。Further, the EEG acquisition subsystem is a single-channel acquisition device that dynamically compensates for EEG signal fluctuations within the normal range and individual differences. side earlobes.
进一步地,所述注意力评价子系统的构成包括:Further, the composition of described attention evaluation subsystem comprises:
提取特征信号模块,用于利用小波变换从EEG信号中提取出特征节律波θ和α;The feature signal extraction module is used to extract the characteristic rhythm waves θ and α from the EEG signal by wavelet transform;
特征样本模块,用于利用初始设定时段采集到的特征节律波θ和α构建特征样本空间;A characteristic sample module is used to construct a characteristic sample space using the characteristic rhythm waves θ and α collected during the initial setting period;
距离计算加权模块,用于计算实时信号与特征样本空间的距离并加权求和;The distance calculation weighting module is used to calculate the distance between the real-time signal and the feature sample space and weight the summation;
模式匹配模块,用于比对所得的距离与预设阀值的关系,匹配得到从注意力集中到注意力涣散两种以上程度的疲劳状态判定结果。The pattern matching module is used to compare the relationship between the obtained distance and the preset threshold, and match to obtain the judgment results of the fatigue state in two or more degrees from concentration to distraction.
进一步地,所述驾驶预警子系统基于智能手机设有驾驶模式输入接口及用于输出预警信号的声光单元、振动单元和自动导航单元。Further, the driving warning subsystem is provided with a driving mode input interface and an acousto-optic unit, a vibration unit and an automatic navigation unit for outputting warning signals based on a smart phone.
本发明实现上述目的的另一种技术解决方案是,司机注意力评价预警方法,与司机的智能手机关联实现,其特征在于包括步骤:Another technical solution for the present invention to achieve the above-mentioned purpose is that the driver's attention evaluation and early warning method is implemented in association with the driver's smart phone, and is characterized in that it includes steps:
脑电采集:利用脑电采集子系统实时记录驾驶状态中司机的EEG信号,并信号处理作为注意力评价的源数据;EEG acquisition: Use the EEG acquisition subsystem to record the driver's EEG signal in real time while driving, and process the signal as the source data for attention evaluation;
注意力评价:利用注意力评价子系统对源数据进行滤波、提取所需波段的时域波形并进行算法分析和分类识别,得到疲劳状态;Attention evaluation: use the attention evaluation subsystem to filter the source data, extract the time-domain waveform of the required band, and perform algorithm analysis and classification identification to obtain the fatigue state;
驾驶预警:利用驾驶预警子系统基于智能手机硬件的APP,根据所得到的司机疲劳状态,并结合设定的驾驶模式通过智能手机反馈对应注意力弱集中状态的预警信息;Driving warning: use the APP of the driving warning subsystem based on the hardware of the smart phone, according to the obtained driver fatigue state, combined with the set driving mode to feed back the warning information corresponding to the state of weak concentration through the smart phone;
三个子系统逐次通信相联且数据交互。The three subsystems communicate and communicate with each other successively and exchange data.
进一步地,所述脑电采集子系统面向注意力评价子系统单向通信,所述注意力评价子系统面向驾驶预警子系统单向通信,且单向通信采用蓝牙的方式实现。Further, the EEG acquisition subsystem is one-way communication oriented to the attention evaluation subsystem, and the attention evaluation subsystem is unidirectional communication oriented to the driving warning subsystem, and the one-way communication is realized by Bluetooth.
进一步地,所述脑电采集针对EEG信号在正常范围内的波动趋势和个体差异动态补偿且采取头套方式将采集点设为司机的侧向前额、参考点设为司机的一侧耳垂,进行单通道采集EEG信号。Further, the EEG acquisition is aimed at dynamically compensating for the fluctuation trend and individual differences of the EEG signal within the normal range, and the headgear method is used to set the acquisition point as the driver's side forehead and the reference point as the driver's side earlobe. channel to collect EEG signals.
进一步地,其特征在于所述注意力评价包括步骤:Further, it is characterized in that the attention evaluation includes steps:
提取特征信号,利用小波变换从EEG信号中提取出特征节律波θ和α;Extract characteristic signals, and use wavelet transform to extract characteristic rhythm waves θ and α from EEG signals;
建立特征样本,利用初始设定时段采集到的特征节律波θ和α构建特征样本空间;Establish characteristic samples, and use the characteristic rhythm waves θ and α collected during the initial setting period to construct the characteristic sample space;
距离计算加权,计算实时信号与特征样本空间的距离并加权求和;Distance calculation weighting, calculating the distance between the real-time signal and the feature sample space and weighting the summation;
模式匹配,比对所得的距离与预设阀值的关系,匹配得到从注意力集中到注意力涣散两种以上程度的疲劳状态判定结果。Pattern matching, compare the relationship between the obtained distance and the preset threshold, and match to obtain the fatigue state judgment results of two or more degrees from concentration to distraction.
进一步地,驾驶预警中基于智能手机设置驾驶模式输入接口及声光单元、振动单元和自动导航单元,司机手动输入或智能手机智能识别驾驶模式,智能手机根据司机疲劳状态的判定结果响应输出语音提示、光色提醒、振动刺激或自动导航引导司机驶入最近的服务区休息。Further, in the driving warning, the driving mode input interface and the sound and light unit, the vibration unit and the automatic navigation unit are set based on the smart phone, the driver manually inputs or the smart phone intelligently recognizes the driving mode, and the smart phone responds to output voice prompts according to the judgment result of the driver's fatigue state , light color reminder, vibration stimulation or automatic navigation to guide the driver into the nearest service area to rest.
应用本发明司机注意力评价及预警方案,具备突出的实质性特点和显著的进步性:本发明采用了便携式脑电采集和分析设备,并且脑电采集设备采用单通道脑电采集方式,使结构更加简单,便于携带,降低了算法的复杂度,提高了司机的舒适度。基于所采集到的脑电信号进行滤波、信号处理、算法分析并得到司机疲劳状态并预警,实现了防患疲劳驾驶的功能,且判断精度高、预警及时、易于操作,可以有效解决高速公路上疲劳驾驶的问题,具有较高的实用价值。The application of the driver's attention evaluation and early warning scheme of the present invention has outstanding substantive features and remarkable progress: the present invention adopts portable EEG acquisition and analysis equipment, and the EEG acquisition equipment adopts a single-channel EEG acquisition method, so that the structure It is simpler and easier to carry, reduces the complexity of the algorithm, and improves the driver's comfort. Filtering, signal processing, and algorithm analysis are performed based on the collected EEG signals to obtain driver fatigue status and early warning, which realizes the function of preventing fatigue driving, and has high judgment accuracy, timely early warning, and easy operation. The problem of fatigue driving has high practical value.
附图说明Description of drawings
图1为本发明司机注意力评价预警系统的架构及通信示意图。FIG. 1 is a schematic diagram of the structure and communication of the driver's attention evaluation and early warning system of the present invention.
图2为脑电采集设备的安装示意图。Figure 2 is a schematic diagram of the installation of the EEG acquisition equipment.
图3为图2另一视角的安装示意图。Fig. 3 is a schematic diagram of installation from another perspective of Fig. 2 .
图4为本发明司机注意力评价预警方法的算法流程图。Fig. 4 is an algorithm flow chart of the driver's attention evaluation and early warning method of the present invention.
图5为智能手机APP初始使用界面示意简图。Figure 5 is a schematic diagram of the initial use interface of the smart phone APP.
图6为智能手机APP一般模式使用界面示意简图。Fig. 6 is a schematic diagram of the general mode user interface of the smart phone APP.
具体实施方式Detailed ways
以下便结合实施例附图,对本发明的具体实施方式作进一步的详述,以使本发明技术方案更易于理解、掌握,从而对本发明的保护范围做出更为清晰的界定和支持。The specific implementation of the present invention will be further described in detail below in conjunction with the accompanying drawings of the embodiments, so as to make the technical solution of the present invention easier to understand and grasp, so as to define and support the protection scope of the present invention more clearly.
本发明是一种基于脑电分析的疲劳驾驶预警系统,如图1所示,本系统主要包括三个部分:脑电采集子系统、注意力评价子系统以及驾驶预警子系统。各个子系统相互独立又相辅相成、可进行数据传输,依据特定的传输协议进行数据交互。脑电采集子系统与注意力评价子系统通过蓝牙设备相连,用于实时采集驾驶状态中驾驶员的脑电信号;注意力评价子系统根据脑电采集子系统得到的数据,进行算法分析,并将分析结果通过蓝牙传输设备发送给驾驶预警子系统。驾驶预警子系统为手机中自主开发的APP软件,驾驶预警子系统在收到注意力评价子系统的数据后,根据收到的数据判断驾驶员是否处于注意力不集中状态。当检测到驾驶员处于注意力不集中状态时,手机振动,并发出警报提醒驾驶员。为便于理解实现方式,从各子系统的实施细节详述如下。The present invention is a fatigue driving early warning system based on EEG analysis. As shown in FIG. 1 , the system mainly includes three parts: an EEG acquisition subsystem, an attention evaluation subsystem and a driving early warning subsystem. Each subsystem is independent and complementary to each other, can carry out data transmission, and carry out data interaction according to a specific transmission protocol. The EEG acquisition subsystem and the attention evaluation subsystem are connected through a Bluetooth device for real-time acquisition of the driver's EEG signals in the driving state; the attention evaluation subsystem performs algorithm analysis based on the data obtained by the EEG acquisition subsystem, and The analysis results are sent to the driving warning subsystem through the bluetooth transmission device. The driving warning subsystem is an APP software independently developed in the mobile phone. After receiving the data from the attention evaluation subsystem, the driving warning subsystem judges whether the driver is in a state of inattention according to the received data. When it detects that the driver is in a state of inattention, the mobile phone vibrates and sends out an alarm to remind the driver. In order to facilitate the understanding of the implementation, the implementation details of each subsystem are described in detail as follows.
一、脑电采集子系统,采用“慢速自适应”算法。针对不同使用者脑电波信号在正常范围内的波动趋势和个体差异进行动态补偿,以进行信号的校准。能够适用于不同的人群和不同的周边环境。在不同的应用场景下都能够具有非常好的准确性和可靠性。采用单导干电极技术,脑电信号采集变得简单易用,佩戴安全、舒适。ThinkGear™从噪音环境中分离出脑电波信号,经过放大处理,产生清晰的脑电波信号。并且采用单通道采集方式,使结构更加简单,便于携带,降低了算法的复杂度,提高了司机的舒适度。1. The EEG acquisition subsystem adopts the "slow self-adaptive" algorithm. Dynamically compensate for the fluctuation trend and individual differences of the brain wave signals of different users within the normal range, so as to calibrate the signals. It can be applied to different groups of people and different surrounding environments. It can have very good accuracy and reliability in different application scenarios. Using single-conductor dry electrode technology, EEG signal acquisition becomes easy to use, safe and comfortable to wear. ThinkGear™ separates the brain wave signal from the noisy environment and amplifies it to produce a clear brain wave signal. Moreover, the single-channel acquisition method is used to make the structure simpler, easy to carry, reduce the complexity of the algorithm, and improve the comfort of the driver.
ThinkGear™ ASIC芯片技术特性:1、采用单导干电极测量技术,2、集成生理信号的滤波和放大功能,3、集成eSense™算法(使用者无需进行额外的信号处理),4、采用工业标准的串行UART输入输出接口,5、输出原始脑电波形数据,数据输出频率可达512Hz功耗低,耗电量小。ThinkGear™ ASIC chip technical features: 1. Using single-lead dry electrode measurement technology, 2. Integrated filtering and amplification functions of physiological signals, 3. Integrated eSense™ algorithm (users do not need to perform additional signal processing), 4. Adopting industrial standards 5. Output the original EEG waveform data, the data output frequency can reach 512Hz, low power consumption and low power consumption.
如图2和图3所示,该脑电采集子系统采集四个导联的脑电信号,采集点位于左前额。参考点为耳垂。带上采集头套,头套将贴紧相应极点。当然实际实施时,对应的左右方向可根据实际情况灵活选择。As shown in Figures 2 and 3, the EEG acquisition subsystem acquires EEG signals from four leads, and the acquisition point is located on the left forehead. The reference point is the earlobe. Put on the collection headgear, the headgear will be close to the corresponding pole. Of course, in actual implementation, the corresponding left and right directions can be flexibly selected according to actual conditions.
二、注意力评价子系统,其具有脑电信号处理、分类识别算法、预警评估等几部分组成,系统中的信号处理算法效率高,体积小,操作方便,因为脑电采集子系统采用了单通道采集的方式,所以智能分类算法具有识别时间短、所需学习数据量较小等优点。2. Attention evaluation subsystem, which consists of several parts such as EEG signal processing, classification recognition algorithm, and early warning evaluation. The signal processing algorithm in the system is efficient, small in size, and easy to operate. The method of channel acquisition, so the intelligent classification algorithm has the advantages of short recognition time and small amount of learning data required.
脑电信号预处理的作用是为了获取所需的脑电信号的数据以及抑制工频干扰和噪声。由于脑电信号强度是微伏级别极其微弱,且极易受到外界50Hz的工频噪声以及0.5Hz以下的极化电平的影响。为了确保结果的准确性,首先对脑电信号进行预处理,采用FIR数字滤波器提取脑电信号的4-30Hz频段,并校正基线漂移。The role of EEG signal preprocessing is to obtain the required EEG signal data and suppress power frequency interference and noise. Since the EEG signal strength is extremely weak at the microvolt level, it is easily affected by external power frequency noise at 50 Hz and polarization levels below 0.5 Hz. In order to ensure the accuracy of the results, the EEG signal was preprocessed first, and the 4-30 Hz frequency band of the EEG signal was extracted by FIR digital filter, and the baseline drift was corrected.
分析子系统还具有智能分类识别算法,将脑电信号转换进行处理分析,将分析结果中的注意力分为不同等级。通过机器学习,不断完善识别算法,将提高识别的稳定性以及精确度。最后将分析结果,即正常状态程度通过蓝牙传输设备实时传送到驾驶预警子系统。该算法主要分为以下几步实现:1)、提取特征信号。利用小波变换从原始脑电信号中提取出特征节律波。2)、建立特征样本空间。利用开始1min提取采集到的特征节律波θ和α构建各自的特征样本空间,用于后续的计算。3)、计算实时观测数据与特征样本空间的距离,并进行加权和。4)、模式匹配,通过以上样本空间距离与阈值的关系确定是否处于疲劳,如果匹配成功,则判定为疲劳,并进行警报,否则继续监测。The analysis subsystem also has an intelligent classification and recognition algorithm, which converts the EEG signals for processing and analysis, and divides the attention in the analysis results into different levels. Through machine learning, continuous improvement of the recognition algorithm will improve the stability and accuracy of recognition. Finally, the analysis result, that is, the degree of normal state, is transmitted to the driving warning subsystem in real time through the Bluetooth transmission device. The algorithm is mainly implemented in the following steps: 1) Extracting characteristic signals. The characteristic rhythm wave is extracted from the original EEG signal by wavelet transform. 2) Establish feature sample space. The characteristic rhythm waves θ and α collected in the first 1 min were used to construct the respective characteristic sample spaces for subsequent calculations. 3) Calculate the distance between the real-time observation data and the feature sample space, and perform a weighted sum. 4) Pattern matching. Determine whether you are fatigued through the relationship between the above sample space distance and the threshold value. If the matching is successful, it will be judged as fatigue and an alarm will be issued, otherwise continue to monitor.
具体操作步骤包括:数据接收、算法识别与数据传输。The specific operation steps include: data receiving, algorithm identification and data transmission.
步骤1:将注意力评价子系统打开,注意力评价子系统中的蓝牙设备自动连接到脑电采集子系统,进而进行数据接收。Step 1: Turn on the attention evaluation subsystem, and the Bluetooth device in the attention evaluation subsystem is automatically connected to the EEG acquisition subsystem to receive data.
步骤2:实验中被测者的状态主要分为两类事件,一类是清醒状态事件,另一类是疲劳状态事件。本发明主要研究的是疲劳状态,其中当由清醒状态进入疲劳状态时θ和β节律波均会出现较大的变化,因此采用θ和β节律波组成各自相应的样本空间,其中由脑电传感器初始设定时段(实施例优选1分钟)采集获得的θ和β特征节律波组成、样本空间,其中θ和β样本值的数学期望分别为、,协方差矩阵为,则实时观测数据x到总体样本的马氏距离为:d(x,)=。Step 2: The states of the subjects in the experiment are mainly divided into two types of events, one is awake state events, and the other is fatigue state events. The main research of the present invention is the state of fatigue, in which both θ and β rhythmic waves will change greatly when entering the fatigued state from the awake state, so the θ and β rhythmic waves are used to form respective corresponding sample spaces, wherein Composition of theta and beta characteristic rhythm waves collected during the initial setting period (preferably 1 minute in the embodiment) , sample space, where the mathematical expectations of the sample values of θ and β are respectively , , the covariance matrix is , then the real-time observation data x to the population sample The Mahalanobis distance is: d(x, )= .
通过上式可以得到实时特征信号到样本空间的马氏距离,,而由于θ和α节律波的马氏距离会随着疲劳状态的变化而出现显著变化,因此考虑将θ和α节律波的马氏距离的加权作为疲劳程度衡定的依据。假设Mda为θ节律波的马氏距离,Mdb为α节律波的马氏距离,则加权距离=λa+(1-λ)b,其中0≤λ≤1,而疲劳判定的阈值则通过实验结果根据ROC曲线分析获得当满足≥时,则可以确定其处于疲劳状态,否则未疲劳,算法流程图如图4所示。Through the above formula, the Mahalanobis distance from the real-time feature signal to the sample space can be obtained , , and because the Mahalanobis distance of θ and α rhythm waves will change significantly with the change of fatigue state, so the weighting of the Mahalanobis distance of θ and α rhythm waves is considered as the basis for determining the degree of fatigue. Assuming that Mda is the Mahalanobis distance of the θ rhythm wave and Mdb is the Mahalanobis distance of the α rhythm wave, the weighted distance =λ a+(1-λ) b, where 0≤λ≤1, and the threshold for fatigue judgment Then the experimental results are obtained according to the ROC curve analysis when satisfying ≥ , it can be determined that it is in a fatigue state, otherwise it is not fatigued, and the algorithm flow chart is shown in Figure 4.
步骤3:无线传输,将注意力等级传输给预警设备。发送端为蓝牙主机,采用一对多发送模式,使得智能预警手环和车载系统同时获得注意力等级。蓝牙传输模块采用CC2540为主芯片,内嵌Bluetooth4.0协议,可以直接与智能手机通信。Step 3: Wireless transmission, transmitting the attention level to the early warning device. The sending end is a Bluetooth host, using a one-to-many sending mode, so that the smart early warning bracelet and the vehicle system can obtain attention levels at the same time. The Bluetooth transmission module uses CC2540 as the main chip, embedded with Bluetooth4.0 protocol, which can directly communicate with smart phones.
三、驾驶预警子系统,其为自主编写手机APP。APP初始界面参考附图5,可以选择APP为一般模式或者高速公路辅助模式。一般模式下,参考附图6,显示的是当前司机的正常驾驶状态,屏幕底侧为蓝牙连接刷新按钮,如果当蓝牙出现连接异常时可以按下此按钮重新连接。其主要作用是将注意力评价子系统生成的驾驶员状态报告由蓝牙传输到智能手机或车载智能系统中反馈给司机,并提醒司机注意请勿疲劳驾驶。当正常状态时,手机不振动,不响铃。当驾驶员轻微疲劳时,手机间歇振动,界面亮黄光,显示轻微分散状态并间歇响铃提醒驾驶员集中注意;当驾驶员处于严重疲劳状态时,手机持续振动,界面亮红光,显示严重疲劳并持续响铃提醒驾驶员应立刻停止行车,并作适当休息。3. The driving warning subsystem, which is a self-written mobile phone APP. Refer to Figure 5 for the initial interface of the APP, and the APP can be selected as the general mode or the highway assistance mode. In normal mode, refer to Figure 6, which shows the current normal driving status of the driver, and the bottom side of the screen is the Bluetooth connection refresh button, if the Bluetooth connection is abnormal, you can press this button to reconnect. Its main function is to transmit the driver status report generated by the attention evaluation subsystem to the smart phone or vehicle intelligent system via Bluetooth to feed back to the driver, and to remind the driver to pay attention not to fatigue driving. When in normal state, the phone does not vibrate or ring. When the driver is slightly fatigued, the mobile phone vibrates intermittently, the interface lights up yellow, showing a slightly distracted state and rings intermittently to remind the driver to pay attention; when the driver is seriously fatigued, the phone continues to vibrate, the interface lights red, showing serious Fatigue and continuous ringing reminds the driver to stop driving immediately and take a proper rest.
当APP处于高速公路辅助模式下,具有一般模式下的功能,并且具有导航功能,APP会智能寻找距离驾驶员最近的服务区,并对驾驶员进行导航,使驾驶员前往服务区,可以得到充分休息,避免交通事故的发生。When the APP is in the highway assist mode, has the functions in the general mode, and has the navigation function, the APP will intelligently find the service area closest to the driver, and navigate the driver so that the driver can go to the service area and get full Rest and avoid traffic accidents.
本发明的有益效果为:本发明采用了便携式脑电采集和分析设备,并且脑电采集设备采用单通道脑电采集方式,使结构更加简单,便于携带,降低了算法的复杂度,提高了司机的舒适度。将脑电采集设备采集到的脑电信号发送到注意力评价子系统进行特征提取进行智能算法分析,应用分类算法将驾驶员所处的驾驶状态分为正常状态、轻微疲劳和严重疲劳三个等级,最终通过蓝牙将分类结果传输到司机的智能手机或车载系统中,并适应性预警动作。本发明真正实现了一款实用的疲劳驾驶预警子系统,且其判断精度高,预警及时,携带方便,易于操作,并且可以有效解决高速公路上疲劳驾驶的问题,具有较高的实用价值。The beneficial effects of the present invention are: the present invention adopts portable EEG acquisition and analysis equipment, and the EEG acquisition equipment adopts a single-channel EEG acquisition mode, which makes the structure simpler, easy to carry, reduces the complexity of the algorithm, and improves the efficiency of the driver. comfort. Send the EEG signals collected by the EEG acquisition equipment to the attention evaluation subsystem for feature extraction and intelligent algorithm analysis, and use the classification algorithm to divide the driving state of the driver into three levels: normal state, mild fatigue and severe fatigue , and finally transmit the classification results to the driver's smart phone or vehicle system via Bluetooth, and adapt the warning action. The invention truly realizes a practical fatigue driving early warning subsystem, and has high judgment accuracy, timely early warning, convenient portability, easy operation, and can effectively solve the problem of fatigue driving on expressways, and has high practical value.
以上详细描述了本发明的优选实施方式,但是,本发明并不局限于上述特定实施方式,本领域技术人员可以在权利要求的范围内进行修改或者等同变换,均应包含在本发明的保护范围之内。The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the above-mentioned specific embodiments, and those skilled in the art can make modifications or equivalent transformations within the scope of the claims, which should be included in the protection scope of the present invention within.
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109394248A (en) * | 2018-12-22 | 2019-03-01 | 江西科技学院 | Driving fatigue detection method and system |
CN109833049A (en) * | 2019-03-05 | 2019-06-04 | 浙江强脑科技有限公司 | Fatigue driving prevention method, device and readable storage medium storing program for executing |
CN114771379A (en) * | 2022-05-19 | 2022-07-22 | 一汽丰田汽车有限公司 | Seat headrest, vehicle and fatigue grade detection method |
CN118902460A (en) * | 2024-09-25 | 2024-11-08 | 广东工业大学 | Attention recognition method based on single-channel electroencephalogram signals and convolutional neural network |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102274032A (en) * | 2011-05-10 | 2011-12-14 | 北京师范大学 | Driver fatigue detection system based on electroencephalographic (EEG) signals |
CN105249961A (en) * | 2015-11-02 | 2016-01-20 | 东南大学 | Real-time driving fatigue detection system and detection method based on Bluetooth electroencephalogram headset |
CN107595306A (en) * | 2017-08-22 | 2018-01-19 | 南京邮电大学 | A kind of driver fatigue monitor system based on electroencephalogramsignal signal analyzing |
-
2018
- 2018-05-11 CN CN201810447393.9A patent/CN108903958A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102274032A (en) * | 2011-05-10 | 2011-12-14 | 北京师范大学 | Driver fatigue detection system based on electroencephalographic (EEG) signals |
CN105249961A (en) * | 2015-11-02 | 2016-01-20 | 东南大学 | Real-time driving fatigue detection system and detection method based on Bluetooth electroencephalogram headset |
CN107595306A (en) * | 2017-08-22 | 2018-01-19 | 南京邮电大学 | A kind of driver fatigue monitor system based on electroencephalogramsignal signal analyzing |
Non-Patent Citations (1)
Title |
---|
冯知音: "脑电信号在身份识别及疲劳检测中的应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109394248A (en) * | 2018-12-22 | 2019-03-01 | 江西科技学院 | Driving fatigue detection method and system |
CN109394248B (en) * | 2018-12-22 | 2022-02-22 | 江西科技学院 | Driving fatigue detection method and system |
CN109833049A (en) * | 2019-03-05 | 2019-06-04 | 浙江强脑科技有限公司 | Fatigue driving prevention method, device and readable storage medium storing program for executing |
CN114771379A (en) * | 2022-05-19 | 2022-07-22 | 一汽丰田汽车有限公司 | Seat headrest, vehicle and fatigue grade detection method |
CN118902460A (en) * | 2024-09-25 | 2024-11-08 | 广东工业大学 | Attention recognition method based on single-channel electroencephalogram signals and convolutional neural network |
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