Fatigue Monitoring System

This work provides an innovative solution for monitoring fatigue for users behind workstations. A web camera was adjusted to work in near infrared range and a system of 880 nm IR diodes was implemented to create an IR vision system to localize and track the eye pupils. The software developed monitors and tracks eyes for signs of fatigue by measuring PERCLOS. The software developed runs on the workstation and is designed to draw limited computational power, so as to not interfere with the user task. To overcome low-frame rate imposed by the hardware limitations and to improve real time monitoring, two-phases detection and tacking algorithm is implemented. The proposed system successfully monitors fatigue at a rate of 8 fps. The system is well suited to monitor users in command centers, flight control centers, airport traffic dispatches, military operation and command centers, etc., but the work can be extended to wearable devices and other environments

Fatigue Monitoring System

This work provides an innovative solution for monitoring fatigue for users behind workstations. A web camera was adjusted to work in near infrared range and a system of 880 nm IR diodes was implemented to create an IR vision system to localize and track the eye pupils. The software developed monitors and tracks eyes for signs of fatigue by measuring PERCLOS. The software developed runs on the workstation and is designed to draw limited computational power, so as to not interfere with the user task. To overcome low-frame rate imposed by the hardware limitations and to improve real time monitoring, two-phases detection and tacking algorithm is implemented. The proposed system successfully monitors fatigue at a rate of 8 fps. The system is well suited to monitor users in command centers, flight control centers, airport traffic dispatches, military operation and command centers, etc., but the work can be extended to wearable devices and other environments

Workers’ Aging Management—Human Fatigue at Work: An Experimental Offices Study

The aging issue in the work context is becoming a significant element of the future sustainability of service and industrial companies. It is well known that with increasing worker age the problem of maintaining the performance and the safety level when fatigue increases is a crucial point, and fatigue increases with the age. Due to social and political developments, especially in Western countries, the retirement age is increasing and companies operate with a higher workforce mean age. Therefore, the problem of recognizing and measuring fatigue has become a key aspect in the management of aging. Note that in the scientific engineering field, the problem of fatigue evaluation when a worker is performing his/her work activities is an important issue in the industrial and service world and especially in the context of the researchers that are investigating the human reliability assessment. As it is clear from the literature, the industrial operations management are suffering from some misleading concepts that only the medicine scientific context can clarify. Therefore, the aim of this paper is to define what are the open issues and the misleading concepts present in the classical fatigue evaluation methods, and second to define two experimental curves of fatigue that will help the decision makers to minimize the impact of fatigue on the workers, thus maximizing the sustainability of the working tasks assigned. This aim is achieved by examining the medical literature about the measurement of a particular kind of fatigue related to the circadian cycle, i.e., the cognitive one; after that, a survey about the possible technologies for measurements is performed. On the basis of technology selection, an experiment on real work activities is performed and some remarkable results about the fatigue in the workers observed and the technology use and its limitations are defined

Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches

Drowsiness and distracted driving are leading factor in most car crashes and near-crashes. This research study explores and investigates the applications of both conventional computer vision and deep learning approaches for the detection of drowsiness and distraction in drivers. In the first part of this MPhil research study conventional computer vision approaches was studied to develop a robust drowsiness and distraction system based on yawning detection, head pose detection and eye blinking detection. These algorithms were implemented by using existing human crafted features. Experiments were performed for the detection and classification with small image datasets to evaluate and measure the performance of system. It was observed that the use of human crafted features together with a robust classifier such as SVM gives better performance in comparison to previous approaches. Though, the results were satisfactorily, there are many drawbacks and challenges associated with conventional computer vision approaches, such as definition and extraction of human crafted features, thus making these conventional algorithms to be subjective in nature and less adaptive in practice. In contrast, deep learning approaches automates the feature selection process and can be trained to learn the most discriminative features without any input from human. In the second half of this research study, the use of deep learning approaches for the detection of distracted driving was investigated. It was observed that one of the advantages of the applied methodology and technique for distraction detection includes and illustrates the contribution of CNN enhancement to a better pattern recognition accuracy and its ability to learn features from various regions of a human body simultaneously. The comparison of the performance of four convolutional deep net architectures (AlexNet, ResNet, MobileNet and NASNet) was carried out, investigated triplet training and explored the impact of combining a support vector classifier (SVC) with a trained deep net. The images used in our experiments with the deep nets are from the State Farm Distracted Driver Detection dataset hosted on Kaggle, each of which captures the entire body of a driver. The best results were obtained with the NASNet trained using triplet loss and combined with an SVC. It was observed that one of the advantages of deep learning approaches are their ability to learn discriminative features from various regions of a human body simultaneously. The ability has enabled deep learning approaches to reach accuracy at human level.

Sitting behaviour-based pattern recognition for predicting driver fatigue

The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers