基于EEG频谱特征的驾驶员疲劳监测研究

研究表明疲劳驾驶是引发交通伤亡事故的重要原因之一,因此有必要采取相应的预防措施。脑电是公认的睡眠(疲劳)金指标,因此论文提出了基于脑电频谱特征的驾驶员疲劳预测方法。采用了驾驶模拟实验中记录的三路驾驶员脑电信号,并利用驾驶员自评与专家评定两种方式相结合的方法将驾驶数据分为疲劳和清醒。针对脑电中眼电噪声很强的特点,对记录的脑电进行了自适应滤波消噪处理,结果显示可有效滤除眼电伪迹;然后根据脑电的频域特征比较突出且与疲劳相关的特点,从去噪后的脑电中提取出了的75个频谱特征;最后利用这些频谱特征,采用朴素贝叶斯分类的方法建立了驾驶员疲劳监测模型。实验结果表明,该方法能监测出驾驶员84%的疲劳状态。     

Are pilots at risk of accidents due to fatigue?

PROBLEM: There is concern in the aviation community that pilot schedules can lead to fatigue and increased chance of an aviation accident. Yet despite this concern, there is little empirical analysis showing the relationship between pilot schedules and commercial aviation accidents. This study attempts to demonstrate an empirical relationship between pilot schedules and aviation accidents. METHOD: Data for human factors-related accidents and pilot work patterns were identified. The distribution of pilot work schedule parameters for the accidents was compared to that for all pilots using a chi-square test to determine if the proportions of accidents and length of duty exposure were the same. If the distributions are the same, then one could infer that pilot human factor accidents are not affected by work schedule parameters. RESULTS: The proportion of accidents associated with pilots having longer duty periods is higher than the proportion of longer duty periods for all pilots. DISCUSSION: There is a discernible pattern of increased probability of an accident as duty time increases for commercial aircraft pilots in the United States. IMPACT ON INDUSTRY: The analysis suggests that establishing limits on duty time for commercial pilots would reduce risk. Such a rule is likely to be expensive and could substantially impact the commercial airlines. In return, there is likely to be a reduction in the risk of commercial aviation accidents due to pilot fatigue.     

Tracing the physiological response and behavioral performance of drivers at different levels of mental workload using driving simulators

Introduction: The use of mobile phones while driving is known to be a distraction factor and a cause of accidents. The way in which different kinds of conversations affect the behavioral performance of the driver as well as the persistence of the effects are not yet fully understood. Method: In this study, in addition to comparing brain function and behavioral function in dual task conditions in three conversations types, the persistent effects of these types of conversations have also been traced. Results: The results show that the content of the mobile phone conversation while driving is the cause of the persistent changes in behavioral and brain functions. Increased time headway and lane departure was observed during and up to 5 min after the emotional conversation was finished. EEG bands also varied in different types of conversations. Cognitive conversations caused an increase in the activity of the alpha and beta bands while emotional conversations enhanced the rate of gamma and beta bands. A meaningful correlation was found between changes in the theta and alpha bands and changes in behavioral performance both during the dual task condition and after the conversation was finished, was also observed. Conclusions: The content of the conversation is one of the most important factors that increase the risk of road accidents. This can also deteriorate the behavioral performance of the driver and can have persistent effects on behavioral performance and the brain. Practical applications: The findings of this study provide a basis to measure and tracing drivers’ cognitive distractions induced by different levels of mental workload through physiological and behavioral performances.     

The relation between upper limb muscle and brain activity in two precision levels of repetitive light tasks

A study was conducted to investigate the effects of repetitive light tasks of low and high precision on upper limb muscles and brain activities. Surface electromyography (EMG) and electroencephalography (EEG) were used to measure the muscle and brain activity of 10 subjects. The results show that the root-mean-square (RMS) and mean power frquency (MPF) of the muscle activity and the mean power of the EEG alpha bands were higher on the high-precision task than on the low-precision one. There was also a high and significant correlation between upper limb muscle and brain activity during the tasks. The longer the time and the more precise the task, the more the subjects become fatigued both physically and mentally. Thus, these results could be potentially useful in managing fatigue, especially fatique related to muscle and mental workload.     

The Relation Between Upper Limb Muscle and Brain Activity in Two Precision Levels of Repetitive Light Tasks

A study was conducted to investigate the effects of repetitive light tasks of low and high precision on upper limb muscles and brain activities. Surface electromyography (EMG) and electroencephalography (EEG) were used to measure the muscle and brain activity of 10 subjects. The results show that the root-meansquare (RMS) and mean power frquency (MPF) of the muscle activity and the mean power of the EEG alpha bands were higher on the high-precision task than on the low-precision one. There was also a high and significant correlation between upper limb muscle and brain activity during the tasks. The longer the time and the more precise the task, the more the subjects become fatigued both physically and mentally. Thus, these results could be potentially useful in managing fatigue, especially fatique related to muscle and mental workload.     

煤矿工人体能负荷与工伤事故关系研究

体力负荷过重而导致疲劳是引起事故不可忽视的重要原因之一.为研究煤矿井下工人体能负荷对工伤事故发生风险的影响,选择北京XX煤矿作为调查对象,测定井下不同岗位矿工的劳动负荷和疲劳指数,回顾性调查近5年内各类工伤事故发生情况,进而分析与工伤事故发生的关系.统计分析结果表明,在疲劳状态下事故发生的危险性明显增加;劳动负荷越重,疲劳越重,事故发生率也相对较高.减轻疲劳对预防事故与保证工作安全具有很重要的意义.     

Distractions and the risk of car crash injury: the effect of drivers' age

PROBLEM: Motor-vehicle accidents are one of the major causes of injury in most motorized countries. Driver distractions have been suggested as a contributor to traffic accidents. Moreover, age of the driver seems to have a role in the relationship between distractions and car crashes. But very few studies have investigated the effect of driver's age on this relationship. This exploratory study investigated the association between distractions, both inside and outside the vehicle, and the increased risk of car crash injury among drivers across different ages. METHOD: This study used a case series design to analyze data routinely collected by the NSW police in Australia. A special focus of this study was on how drivers' age affects the risk of car crash injury, which was determined by using a well-documented risk estimation methodology. RESULTS: The results obtained indicated that drivers of all ages, on the whole, are more susceptible to distractions inside the vehicle than distractions coming from outside. Age was shown to affect the relationship between in-vehicle distraction and the risk of car crash injury. A separate analysis was also conducted on hand-held phone usage while driving with results supplementing previous findings reported in the literature. IMPACT TO INDUSTRY: Safety strategies to countermeasure in-vehicle distractions have been suggested and discussed.     

基于计算机视觉的驾驶疲劳识别方法的研究

汽车驾驶员疲劳驾驶是引发交通事故的重要原因之一,许多国家在进行疲劳报警器的研究。在介绍和比较多种驾驶疲劳测评方法的基础上,阐述PERCLOS评价驾驶疲劳的机理,并与其他几种方法进行对比,以证明其优越性。设计一套驾驶员疲劳监控系统,应用图像差分的方法,灰度直方图、直方图均衡化等图像分析手段,定位和识别驾驶员眼睛睁开与闭合的变化过程,统计出眼睛闭合时间来确定疲劳程度,并通过利用MATLAB得以实现。给出了一种基于PERCLOS的驾驶员疲劳识别的新方法。     

疲劳驾驶交通事故的严重程度影响因素分析

为探究和定量分析疲劳驾驶交通事故严重程度的影响因素,以广东省1370条疲劳驾驶事故数据为基础,对比分析不同年份、时间段以及年龄段的疲劳驾驶交通事故特征;以交通事故严重程度为因变量,将其分为严重事故和非严重事故,从驾驶员年龄、驾龄、车辆类型等17个初步选择的自变量中筛选对疲劳驾驶交通事故严重程度具有显著影响的因素;采用二...     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

TORAP—a new tool for conducting rapid risk assessment in petroleum refineries and petrochemical industries

The use of a new computer-automated tool TORAP (TOol for Rapid risk Assessment in Petroleum refinery and petrochemical industries) is demonstrated through a rapid and quantitative risk assessment of a typical petroleum refinery. The package has been applied for an appraisal of the risks of accidents (fires, explosions, toxic release) posed by different units of the refinery, and to identify steps to prevent/manage accidents. The studies reveal that TORAP enables a user to quickly focus on the accidents likely to occur, and enables forecasting the nature and impacts of such accidents. This information is directly utilisable in identifying “soft” spots and in taking appropriate remedial measures to prevent or control accidents. The special attributes of TORAP are: (a) a wide range of applications—achieved by incorporating models capable of handling all types of industrial fires and explosions, (b) sophistication—brought about by including state-of-the-art models developed by these authors and others, (c) user-friendliness—achieved by incorporating on-line help, graphics, carefully formatted output, and, above all, an automatic module with which even a lay user can conduct risk assessment. The entire package, especially its automatic module, is supported by an extensive knowledge-base built into the software.     

Improving an algorithm for classifying error types of front-line workers: Insights from a case study in the construction industry

The objective of this study was to propose improvements in an algorithm for classifying error types of front-line workers. The improvements involved: (a) making recommendations on organizing the data needed to apply the algorithm (e.g. identify actions and decisions that may serve as a reference for analysing the types of errors) and (b) drawing up guidelines for interpreting the questions that are part of the algorithm (e.g. how to define what counts as a procedure). The improvements were identified on the basis of testing the algorithm on construction sites, an environment in which it had not yet been implemented. Thus, 19 occupational accidents which had occurred in a small-sized construction company were investigated and the error types of both workers who had been injured and crew members were classified. The accidents investigated were used as a basis both to illustrate how the improvements proposed should be put in practice and to illustrate how practical insights for safety management might be derived from the algorithm.     

Can SVM be used for automatic EEG detection of drowsiness during car driving?

This study aims to develop an automatic method to detect drowsiness onset while driving. Support vector machines (SVM) represents a superior signal classification tool based on pattern recognition. The usefulness of SVM in identifying and differentiating electroencephalographic (EEG) changes that occur between alert and drowsy states was tested. Twenty human subjects underwent driving simulations with EEG monitoring. Alert EEG was marked by dominant beta activity, while drowsy EEG was marked by alpha dropouts. The duration of eye blinks corresponded well with alertness levels associated with fast and slow eye blinks. Samples of EEG data from both states were used to train the SVM program by using a distinguishing criterion of 4 frequency features across 4 principal frequency bands. The trained SVM program was tested on unclassified EEG data and subsequently checked for concordance with manual classification. The classification accuracy reached 99.3%. The SVM program was also able to predict the transition from alertness to drowsiness reliably in over 90% of data samples. This study shows that automatic analysis and detection of EEG changes is possible by SVM and SVM is a good candidate for developing pre-emptive automatic drowsiness detection systems for driving safety.     

Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue

Introduction: Fatigue is one of the most crucial factors that contribute to a decrease of the operating performance of aircraft pilots and car drivers and, as such, plays a dangerous role in transport safety. To reduce fatigue-related tragedies and to increase the quality of a healthy life, many studies have focused on exploring effective methods and psychophysiological indicators for detecting and monitoring fatigue. However, those fatigue indicators rose many discrepancies among simulator and field studies, due to the vague conceptualism of fatigue, per se, which hinders the development of fatigue monitoring devices. Method: This paper aims to give psychological insight of the existing non-invasive measures for driver and pilot fatigue by differentiating sleepiness and mental fatigue. Such a study helps to improve research results for a wide range of researchers whose interests lie in the development of in-vehicle fatigue detection devices. First, the nature of fatigue for drivers/pilots is elucidated regarding fatigue types and fatigue responses, which reshapes our understanding of the fatigue issue in the transport industry. Secondly, the widely used objective neurophysiological methods, including electroencephalography (EEG), electrooculography (EOG), and electrocardiography (ECG), physical movement-based methods, vehicle-based methods, fitness-for-duty test as well as subjective methods (self-rating scales) are introduced. On the one hand, considering the difference between mental fatigue and sleepiness effects, the links between the objective and subjective indicators and fatigue are thoroughly investigated and reviewed. On the other hand, to better determine fatigue occurrence, a new combination of measures is recommended, as a single measure is not sufficient to yield a convincing benchmark of fatigue. Finally, since video-based techniques of measuring eye metrics offer a promising and practical method for monitoring operator fatigue, the relationship between fatigue and these eye metrics, that include blink-based, pupil-based, and saccade-based features, are also discussed. To realize a pragmatic fatigue detector for operators in the future, this paper concludes with a discussion on the future directions in terms of methodology of conducting operator fatigue research and fatigue analysis by using eye-related parameters.     

A pre/post evaluation of fatigue,stress and vigilance amongst commercially licensed truck drivers performing a prolonged driving task

Purpose. The main purpose of this research study was to evaluate changes in fatigue, stress and vigilance amongst commercially licensed truck drivers involved in a prolonged driving task. The secondary purpose was to determine whether a new ergonomic seat could help reduce both physical and cognitive fatigue during a prolonged driving task. Two different truck seats were evaluated: an industrial standard seat and a new truck seat prototype. Methods. Twenty male truck drivers were recruited to attend two testing sessions, on two separate days, with each session randomized for seat design. During each session, participants performed two 10-min simulated driving tasks. Between simulated sessions, participants drove a long-haul truck for 90 min. Fatigue and stress were quantified using a series of questionnaires whereas vigilance was measured using a standardized computer test. Results. Seat interactions had a significant effect on fatigue patterns. Conclusion. The new ergonomic seat design holds potential in improving road safety and vehicle accidents due to fatigue-related accidents.     

基于粗糙集与遗传算法的航空器空中相撞事故因素分析

为高效准确挖掘航空器空中相撞事故与相应事故致因因素间的规律,利用事故数据,提出一种基于粗糙集和遗传算法的分析模型。首先运用粗糙集理论对SHELL模型提供的第一层次因素进行重要性程度排序,并确定其权重;然后分析事故中各环节的具体因素,结合遗传算法对这些子因素进行约简,约简时重点考虑重要度高的主因素,确定最终的事故因素及其决策规则;最后,实例分析得出导致事故的主要因素为机组违规、管制差错与违规、气象和空域。实例分析结果表明,该模型与算法能适应航空事故数据特点,能客观描述几个主要因素与事故发生之间的关联关系。     

A survey of forest workers in New Zealand. Do hours of work, rest, and recovery play a role in accidents and injury?

PROBLEM: A number of structural and organizational changes have occurred recently within the New Zealand Forestry Industry, with concerns being raised about the impact of these changes on the forestry worker in terms of fatigue, sleepiness, and compromised safety. This study explored the relationship of fatigue, and some of its key determinants, with accidents and injuries in a group of forestry industry workers in New Zealand. METHOD: A total of 367 forestry workers responded to a self-administered questionnaire. RESULTS: Fatigue was found to be commonly experienced at work in the forest, with 78% of workers reporting that they experienced fatigue at least "sometimes." This study found that certain groups of workers reported long working hours, reduced sleep, compromised recovery time, and intensely paced work. The results of logistic regression analysis showed that recent sleep, number of breaks taken during the workday, and specific job/tasks were independently associated with reporting of high fatigue levels at work. Near-miss injury events were significantly more common among those reporting a high level of fatigue at work. Accidents and lost-time injury were associated with length of time at work, ethnicity, and having had near-miss injury events. DISCUSSION: Together, these results suggest that fatigue and aspects of work organization, which are likely to be fatiguing, may be associated with compromised safety for forest workers. IMPACT ON INDUSTRY: With an already slim margin of error present in forest operations, an impairment due to increased fatigue may constitute a significant risk factor for accidents and injuries in this workforce. The results indicate the need for further examination of shift and workload management among forestry workers, as well as a role for improving industry awareness about the causes and consequences of fatigue.     

An algorithm for assessing the risk of traffic accident

INTRODUCTION: This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area. METHOD: The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm. RESULTS: The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis. IMPACT ON INDUSTRY: The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.     

Worker-fatigue contributing to workplace incidents in New Zealand Forestry

ProblemReports of incidents in dangerous work environments can be analysed to identify common hazards, in turn aiding in the prevention of future accidents. Whilst studies exist that do this, most focus on causes that involve physical risks. In this paper we propose an alternative approach, and illustrate causes of forestry incidents from the perspective of worker-failure and fatigue. Method This paper outlines the analysis of eight years’ worth of New Zealand forestry incident data, with a focus on the cause of, and time that, incidents occur. Results This has resulted in two main findings. First, 70% of incidents can be attributed, at least in part, to worker-failures. Second, 78% of worker-failure based causes show indications of fatigue. This indicates that a significant number of forestry incidents are caused by worker-fatigue. Finally, this dataset showed inconsistencies in data quality, similar to those that exist in other datasets. This did not affect our analysis. However, these types of errors have the potential to affect the data quality in the national reporting system. Impact on industry The results from this study will be used in a larger project on detecting fatigue in forestry workers for injury and incident prevention. It is also our hope that other researchers may find these results of interest for further fatigue prevention research in hazardous industries.