EEG-based drowsiness estimation for safety driving using independent component analysis

Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.     

Seat Belt Vibration as a Stimulating Device for Awakening Drivers

This paper presents a safety driving system that uses a seat belt vibration as a stimulating device for awakening drivers. The vibration stimulus was composed of pulsation tension, which was applied by the seat belt motor retractor. Magnitude, duration, and repetition rate of the additional tension were the major parameters that determined the awakening effect of the stimulus. We constructed a driving simulator, which was able to induce driver's drowsiness. In the experiments using the driving simulator, the driver's drowsiness was detected by changes in the driver's eye movements measured by electrooculography (EOG) and/or changes in facial expression of the driver monitored by the examiners through a video camera, subjective evaluation, and lane deviation. Exerting additional tension of 130 N for 3 cycles at duration and interval of 100 ms was the most effective pattern for awakening the driver without causing discomfort.     

Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles

This paper deals with the development of Human-Centric Intelligent Driver Assistance Systems. Rear-end collisions account for a large portion of traffic accidents. To help mitigate this problem, predictive braking systems and adaptive cruise control systems have been developed. However, these types of systems usually rely solely on the vehicle and vehicle surround sensors, either ignoring the human component of driving or learning the driver's control behavior using only these sensors. As with all human-computer interfaces, this has the potential to work against the driver, distract the driver further, or even annoy the driver so that the driver ignores or disables the system. It is, therefore, important to directly take the driver's intended actions into account when designing a driver assistance system. By using a probabilistic model for the system, warnings and preventative measures can be constructed based on varying levels of situational severity and driver attentiveness and intent. The research is based upon carefully conducted experimental trials involving a human subjects driving in natural manner and on typical freeways in the USA. The experiments, designed by inputs from cognitive scientist, were conducted in a specially designed instrumented vehicle to record important cues associated with driver's behavior, vehicle state, and vehicle surround in a synchronized manner. Quantitative results and analysis of the experimental trials are presented to show the feasibility and promise of this framework to predict the driver's intent to brake, the need for braking given the current situation, and at what level the driver should be warned     

ECG on the road: robust and unobtrusive estimation of heart rate

Modern automobiles include an increasing number of assistance systems to increase the driver's safety. This feasibility study investigated unobtrusive capacitive ECG measurements in an automotive environment. Electrodes integrated into the driving seat allowed to measure a reliable ECG in 86% of the drivers; when only (light) cotton clothing was worn by the drivers, this value increased to 95%. Results show that an array of sensors is needed that can adapt to the different drivers and sitting positions. Measurements while driving show that traveling on the highway does not distort the signal any more than with the car engine turned OFF, whereas driving in city traffic results in a lowered detection rate due to the driver's heavier movements. To enable robust and reliable estimation of heart rate, an algorithm is presented (based on principal component analysis) to detect and discard time intervals with artifacts. This, then, allows a reliable estimation of heart rate of up to 61% in city traffic and up to 86% on the highway: as a percentage of the total driving period with at least four consecutive QRS complexes.     

Estimating alertness from the EEG power spectrum

In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings     

基于EOG的安全辅助驾驶系统算法设计与实现

为保证驾驶安全,提高车辆控制系统的智能化水平,实现“手不离盘”操作,设计并实现了一种基于眼电图(EOG)的安全辅助驾驶系统。该系统利用安装在驾驶员眼睛周围的生物电极采集其在观测抬头显示器(HUD, head up display)上提示符时所产生的扫视信号,生成多种车载设备控制命令;对原始多导联EOG信号进行端点检测后,使用了独立分量分析(ICA, independent component analysis)方法进行空域滤波后提取眼动信号特征参数,并结合支持向量机实现了上、左与右扫视动作的识别。实验室环境下对所提算法进行了测试,15位受试者在疲劳与非疲劳状态下的在线平均正确率达到了98.43%与96.0%。实验结果表明,基于ICA多类扫视信号识别算法的安全辅助驾驶系统在眼动信号分析中呈现出了良好的分类性能。     

EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment

Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.     

基于DOAS的消防应急救援多气体快速遥感仪

在国内首次报道了可对30多 种有毒气体连续监测的基于DOAS(differential optical absorption spectra)技术的车载消防应急救援多气体快速遥感仪,仪器采用长光程紫外差分吸收光谱 法痕量气体分析 技术,光发射器和光接收器共用一个卡塞格林望远镜,以减少大气紊动对测量数据的影响; 仪器的反射系 统可以灵活地设置在任意需要区域,分析系统设置在救援车内,在需要长时间监测时,避免 了工作人员近 距离暴露在危险环境中。多气体DOAS仪器特别适合各类气体泄漏事故的消防应急处理。     

山区公路条件下驾驶员生理特性研究

公路环境直接或间接地影响驾驶员的心率变化,继而影响其行为反应。针对山区公路坡长路陡、弯道多、落差大、视距不足,易引发交通事故的特点,文中结合医学与心理学的理论,运用图形化生物医学工程实验平台进行实车驾驶员心率采集,并对采集到的心率的变异性和增长率进行分析研究。结果表明,在相同实验条件下,不同的山区公路环境对驾驶员的生理指标存在差异。该实验平台操作简单、能实时有效地采集信号、内置分析功能强大、采集数据精确等特点。该研究对山区公路线形设计具有一定的现实指导意义。     

Noninvasive Neural Prostheses Using Mobile and Wireless EEG

Neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunctioning external body parts or internal organs, a much larger population of ldquohealthyrdquo people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses. For example, reduced alertness, lack of attention, or poor decision-making during monotonous, routine tasks can have catastrophic consequences. This study proposes a noninvasive mobile prosthetic platform for continuously monitoring high-temporal resolution brain dynamics without requiring application of conductive gels on the scalp. The proposed system features dry microelectromechanical system electroencephalography sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, online artifact cancellation, and signal processing. Its implications for neural prostheses are examined in two sample studies: 1) cognitive-state monitoring of participants performing realistic driving tasks in the virtual-reality-based dynamic driving simulator and 2) the neural correlates of motion sickness in driving. The experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.     

Data Supply Voltage Reduction Scheme for Low‐Power AMOLED Displays

This paper demonstrates a new driving scheme that allows reducing the supply voltage of data drivers for low‐power active matrix organic light‐emitting diode (AMOLED) displays. The proposed technique drives down the data voltage range by 50%, which subsequently diminishes in the peak power consumption of data drivers at the full white pattern by 75%. Because the gate voltage of a driving thin film transistor covers the same range as a conventional driving scheme by means of a level‐shifting scheme, the low‐data supply scheme achieves the equivalent dynamic range of OLED currents. The average power consumption of data drivers is reduced by 60% over 24 test images, and power consumption is kept below 25%.     

时变混合共轭梯度盲提取算法

针对传统独立分量分析(ICA)方法对时变信道跟踪能力较差的问题,提出了一种时变混合共轭梯度盲提取算法。该算法有效利用了各源信号的时序结构差异,仅利用其二阶统计量解决了具有不同功率谱密度的信号的分离,而无须估计信号的概率密度和计算高阶累积量,减少了运算的复杂度并可用于杂系信号混合的盲分离问题;同时,算法利用仅具有一个全局最优解的凸代价函数,采用计算简单并具有较好数值表现的自适应共轭梯度算法进行迭代,获得了更快的收敛速度和更好的稳定性能。仿真结果表明,该算法与传统ICA算法相比,具有对时变系统更好的跟踪能力。      

基于ARM的防疲劳驾驶检测系统

基于ARM的防疲劳驾驶检测系统,采用USB摄像头采集驾驶员的面部图像并将图像转换为数字信号传给ARM处理器。采集的面部图像通过模板匹配算法用特定的眼部分类器进行计算,与眼部分类器配比,在指定的矩形区域中提取出眼部图像,计算眼部图像的灰度直方图。通过对驾驶员的眼部闭合状态的实时分析可以获得PERCLOS参数,从而判断驾驶员的驾驶疲劳状态。经实验测试证明该方法准确性高,能在多种情况下检测驾驶员是否处于驾驶疲劳状态。     

Improving Solar Power Generation and Defects Detection Using a Smart IoT System for Sophisticated Distribution Control (SDC) and Independent Component Analysis (ICA) Techniques

These days, peoples are more concerned respects petroleum product energy and conservational issues caused on the power generation networks and renewable power resources at any other time. Amongst the renewable power resources, solar and windmill power generations are essential competitors. Photovoltaic modules additionally have moderately least transformation effectiveness. General system price was decreased utilizing significant productivity control which are made to determine for most significant achievable energy from solar PV array module utilizing MPPT procedures. Existing solar power generation likewise have the burden of being for the day outputs is less immediate introduction from natural sun radiation. By utilizing the Internet of Things (IoT) strategies for monitoring and controlling the solar power generation was significantly enhance the performance, and maintenance of the solar power plant. In this work explicitly argue advances IoT technique to increase output result of solar power generation at the system level. Covering turning the photovoltaic system in the position of maximum sunlight, obtaining significant available power obtained from the solar PV array and significant battery health management by using sophisticated distribution control (SDC) and independent component analysis techniques (ICA).The simulation work done under with the MATLAB software using proposed SDC and ICA logics the simulation results demonstrate the efficiency of the proposed method and its ability to track the maximum power of the PV panel. Over 97% efficiency achieved by using SDC and ICA methods.     

A driving behavior detection system based on a smartphone's built‐in sensor

Traffic accidents resulting from driving behavior and road conditions are crucial problems for drivers. The causes and responses to traffic accidents have been widely studied by researchers. Whereas several approaches have been proposed to ease these problems, most works entail high computational costs or rigid hardware conditions. To address these challenges, we propose Health Driving , a smartphone‐based system for detecting driving events and road conditions solely with a built‐in smartphone acceleration sensor. More specifically, we first collect acceleration data from the acceleration sensor of a smartphone on a vehicle, and then utilize an acceleration reorientation calibration algorithm to convert the obtained acceleration data from the smartphone to acceleration data of the vehicle. Finally, we exploit Health Driving to detect driving events and road conditions, and evaluate the seriousness of the road conditions and driving events by using an efficient scoring mechanism based on the ISO 2631 standard. An extensive evaluation demonstrates that Health Driving operates successfully with an ordinary smartphone, and operates with a low computational cost compared with other methods. Copyright © 2016 John Wiley & Sons, Ltd.     

雾霾天气下降质图像的清晰化处理

针对雾霾天气条件下大气散射和悬浮颗粒物引起的图像对比度、清晰度降低问题,提出通过采用独立分量分析( ICA)算法分解图像中具有相互独立分量的混合像元,估计图像有用信息分量。因乘性噪声信息的存在,会导致估计的图像有用信息有一定的偏差;采用基于噪声检测的局部自适应中值滤波对估计的图像有用信息进行了进一步校正。实验结果证明：新算法能有效去除雾霾,提高图像的清晰度和对比度。     

面向智能电网架空线的传感器故障容忍机制

在配用电网络全网的监控过程中,杆塔等设施的状态监测与故障容忍成为电力系统亟待解决的问题。现有的监控系统由于网络线性拓扑结构等限制,故障发生时无法及时维护,影响到电力生产业务,易造成电力重大事故。该文面向利用传感器监控电网架空线的背景,提出一个针对传感器部署的故障容忍机制。首先,依据N-x原则等,最小化冗余备份节点和无线模块的数量,达到成本最小化的目的。其次,综合考虑时延约束、N-x原则的数量约束等构建数学优化模型。基于该模型,利用聚类合并思想,构建了一个面向智能电网架空线的传感器故障容忍机制。最后,仿真实验证明,以此机制部署的传感器监测网络能够在成本最小化的基础上,有效地容忍故障。     

基于ARM和信息融合技术的矿井环境监测系统

针对煤矿井下的环境复杂性,提出一种低成本、低功耗和高性能的网络化安全监测与预警系统很必要。介绍了一种基于三星公司32位ARM处理器的用于煤矿环境监测与预警的解决方案,提出了硬件构成及相关算法的实现方法,该系统通过在ARM中进行多传感器信息融合处理,利用两级融合进行多传感器状态识别,最后通过开滦集团钱家营煤矿数据作应用,证明了系统的实用性和可靠性。同时利用ARM良好的网络集成性能将现场局域网通过以太网与远程监控主机互联,实现系统的监控、决策网络化,优化和提高了系统的使用性能。     

电路板故障诊断系统接口连接组件的设计

随着大规模及超大规模集成电路的研制与生产,自动测试技术也显得越来越重要。为解决众多雷达电路板共用一套测试系统,接口连接组件的设计成为自动测试系统的关键部分之一。通过分析众多雷达电路板故障诊断要求,以某雷达电路板故障诊断系统为依据,介绍了一种接口连接组件的设计方法。实验表明,设计的接口连接组件操作简单﹑适应性强,符合自动测试发展要求,具有很强的实用性。     

基于Robei EDA工具与FPGA的倒车预警系统设计

基于对汽车倒车预警技术的研究,具有重要的现实意义。本文提出一种基于FPGA技术的超声倒车预警机器人,具有大视野实时监控测距、减少行车盲区的功能,可以更大范围地避免倒车事故。FPGA作为数据处理核心,在摄像头捕捉到人体时通过人体识别算法可以在显示屏上将人体自动框出,提示驾驶人员注意安全。超声波测距模块实时监测车辆与后方障碍物的距离及方位信息,当距人体之间的距离小于一定距离的时候,将通过蜂鸣器发出警报,起到了辅助倒车的作用。此外,系统还可以对车内环境参数进行测量并将数据传输至上位机显示。测试结果表明,系统完成了在倒车过程中对人体的识别与报警,具有高速、实时性高、可靠性高等优点。