Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

Real-time drowsiness detection using wearable, lightweight EEG sensors

Driver drowsiness has always been a major concern for researchers and road use administrators. It has led to countless deaths accounting to significant percentile of deaths world over. Researchers have attempted to determine driver drowsiness using the following measures: (1) subjective measures (2) vehicle-based measures; (3) behavioral measures and (4) physiological measures.;Studies carried out to assess the efficacy of all the four measures, have brought out significant weaknesses in each of these measures. However detailed and comprehensive review has indicated that Physiological Measure namely EEG signal analysis provides most reliable and accurate information on driver drowsiness. In this paper a brief review of systems, and issues associated with them has been discussed with a view to evolve a novel system based on EEG signals especially for use in mine vehicles.;The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable EEG sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. But the use of light-weight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines is shown to classify drowsy states with high accuracy.;The system is validated using data collected on 23 subjects in fresh and drowsy states. The EEG signals are also used to characterize the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Modern drowsiness detection techniques: a review

According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness

Analyzing human factors in road accidents: TRACE WP5 Summary Report

The main objectives of TRACE WP5 'Human factors' deliverables are: i) To support a better standardization of accident analysis in Europe on a scientific background, ii) To provide operational models and methodological classification grids dealing with 'human factors' aspects involved in road accidents, iii) To promote a comprehensive analysis of the involvement of human beings, going further than the usual 'user-orientated causal analysis' often limited at establishing the driver 'at fault' and without searching for the background reasons of the problems met par road users. Such objectives involve analyzing accidents as the symptom of the difficulties met by drivers in certain driving situations, and as a revelatory of their needs in help. Two questions have to be asked in order to progress in the understanding of accident causation: 1) What are precisely and operationally the human failures in accidents? But also: 2) What are the reasons for these human failures? Keeping in mind that these reasons are of multiple natures and combine most of the time to produce the final event. By so doing, the definition of typical scenarios of 'human error' production can open to the definition of more appropriate countermeasures, well fitted to human needs