Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram

The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds)

IMPROVED PSO BASED DRIVER’S DROWSINESS DETECTION USING FUZZY CLASSIFIER

In this drowsiness detection framework two actions including brain and visual features are utilised to distinguish the various levels of drowsiness. These actions are provided by the EEG and EOG signal brain actions. From the EEG and EOG signals the peculiarities like mean, peak, pitch, maximum, minimum, standard deviation are assessed . In these peculiarities we decide on some best attributes - peak and pitch employing an IPSO strategy that picks up the best threshold esteem. These signals are then offered into the STFT which is employed to discover the signal length, producing a STFT network from the intermittent hamming window,the output of which are energy signals alpha and beta. These energy signals are offered into the MCT to get an alpha mean and a beta mean -the most chosen and outstanding attributes. These are then subjected to fuzzy based classification to give a precise result checking over the maximum values in the alpha and the beta series . &nbsp

Drowsy driver data acquisition system

This thesis focuses on detecting the drowsiness of a driver based on differentiation of the EEG signal activity between the eyes open and eyes closed states. Here, it is observed that there is a significant increase \u27in a 10 Hz component of the alpha rhythm activity when the subject under test closes his / her eyes. This phenomenon was observed when electrodes were attached to the occipital region. A more desirable approach is to develop a non-intrusive measurement based on a multiturn differential coil combination utilizing a low noise high gain amplifier. The system developed here used an 80,000 turn 2 coil differential combination. A 10 Hz band pass amplifier with a gain of 68 db confirmed the assumed changes when electrodes were used. However, when differential coils were used (80,000 differential coils), the system failed to validate the expected changes. Due to insufficient sensitivity, it was impossible to reach a conclusion and determine whether the increased 10 Hz activity corresponded to brain signals or increased feedback gain resulting in an internal oscillation within the high gain amplification of the developed system. Further studies are suggested to reduce the losses due to magnetic core material and design an amplifier with a lower noise figure. The system developed utilized a DaqCard-1200 data acquisition card and MATLAB for signal processing

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

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

Assessing Ratio-Based Fatigue Indexes Using a Single Channel EEG

Driver fatigue is a state of reduced mental alertness which impairs the performance of a range of cognitive and psychomotor tasks, including driving. According to the National Highway Traffic Safety Administration, driver fatigue was responsible for 72,000 accidents that lead to more than 800 deaths in 2015. A reliable method of driver fatigue detection is needed to prevent such accidents. There has been a great deal of research into studying driver fatigue via electroencephalography (EEG) to analyze brain wave data. These research works have produced three competing EEG data-based ratios that have the potential to detect driver fatigue. Research has shown these three ratios trend downward as fatigue increases. However, no empirical research has been conducted to determine whether drivers begin to feel fatigue at a certain Percent Change from an alert state to a fatigue state in one or more of these ratios. If a Percent Change could be identified for which drivers begin to feel fatigue, then it could be used as a method of fatigue detection in real-time system. This research focuses on answering this question by collecting brain wave data via an EEG device over a 60-minute driving session for 10 University of North Florida (UNF) students. A frequency distribution and cluster analysis was done to identify a common Percent Change for the participants who experienced fatigue. The results of the analysis were compared to a subset of users who did not experience fatigue to validate the findings. The project was approved by the UNF IRB on Nov. 1, 2016 (reference number 475514-4)

Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (