Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin

An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver

In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.Comment: 4 pages, 2 figures, edited version of published paper in IEEE ICITE 201

Analysis of yawning behaviour in spontaneous expressions of drowsy drivers

Driver fatigue is one of the main causes of road accidents. It is essential to develop a reliable driver drowsiness detection system which can alert drivers without disturbing them and is robust to environmental changes. This paper explores yawning behaviour as a sign of drowsiness in spontaneous expressions of drowsy drivers in simulated driving scenarios. We analyse a labelled dataset of videos of sleep-deprived versus alert drivers and demonstrate the correlation between hand-over-face touches, face occlusions and yawning. We propose that face touches can be used as a novel cue in automated drowsiness detection alongside yawning and eye behaviour. Moreover, we present an automatic approach to detect yawning based on extracting geometric and appearance features of both mouth and eye regions. Our approach successfully detects both hand-covered and uncovered yawns with an accuracy of 95%. Ultimately, our goal is to use these results in designing a hybrid drowsiness-detection system

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

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results

Numerical Simulation and Design of Machine Learning Based Real Time Fatigue Detection System

The proposed research is a step to implement real time image segmentation and drowsiness with help of machine learning methodologies. Image segmentation has been implemented in real time in which the segments of mouth and eyes have been segmented using image processing. Input can be provided by the help of real time image acquisition system such as webcam or internet of things based camera. From the video input, image frames has been extracted and processed to obtain real time features and using clustering algorithms segmentation has been achieved in real time. In the proposed work a Support Vector Machine (SVM) based machine learning method has been implemented emotion detection using facial expressions. The algorithm has been tested under variable luminance conditions and performed well with optimum accuracy as compared to contemporary research

DRIVER DROWSINESS DETECTION BY USING WEBCAM

Drowsiness and fatigue are one of the main causes leading to road accidents. They can be prevented by taking effort to get enough sleep before driving, drink coffee or energy drink, or have a rest when the signs of drowsiness occur. The popular drowsiness detection method uses complex methods, such as EEG and ECG. This method has high accuracy for its measurement but it need to use contact measurement and it has many limitations on driver fatigue and drowsiness monitor [18]. Thus, it is not comfortable to be used in real time driving. This paper proposes a way to detect the drowsiness signs among drivers by measuring the eye closing rate and yawning