Using Machine Learning to Determine the Motorist Somnolence

Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organ-ization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection sys-tem that can effectively minimize accidents associated with exhaustion. The system uti-lizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined thresh-old, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi fa-cilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to miti-gate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel

Project Awakesure: Intelligent Drowsiness Detection Using Eye Tracking

Being sleepy or drowsy is referred to as being drowsy. A person who is sleepy may feel exhausted or lethargic and struggle to stay awake. People who are sleepy tend to be less attentive and may even nod off, though they can still be awakened. An increasing number of vocations nowadays call for sustained focus. In order for drivers to respond quickly to unexpected incidents, they must maintain a watchful eye on the road. Many road incidents are directly caused by tired drivers. In order to drastically lower the frequency of fatigue-related auto accidents, it is crucial to develop technologies that can identify and alert a driver to a poor psychophysical state. However, there are many challenges in developing systems that can quickly and accurately recognize a driver's signs of fatigue. Using vision-based technology is one technological option for implementing driver fatigue monitoring systems. The available driver drowsiness detection systems are described in this article. Here, we are assessing the driver's level of sleepiness utilizing his visual system. The automated system for preventing accidents and monitoring sleepy drivers developed for this study is based on detecting variations in the length of eye blinks. Our recommended technique makes use of the eyes' postulated horizontal symmetry property to identify visual changes in eye positions. Our novel approach precisely positions a standard webcam in front of the driver's seat to identify eye blinks. It will identify the eyeballs based on a specific EAR (Eye Aspect Ratio)

A sophisticated Drowsiness Detection System via Deep Transfer Learning for real time scenarios

Driver drowsiness is one of the leading causes of road accidents resulting in serious physical injuries, fatalities, and substantial economic losses. A sophisticated Driver Drowsiness Detection (DDD) system can alert the driver in case of abnormal behavior and avoid catastrophes. Several studies have already addressed driver drowsiness through behavioral measures and facial features. In this paper, we propose a hybrid real-time DDD system based on the Eyes Closure Ratio and Mouth Opening Ratio using simple camera and deep learning techniques. This system seeks to model the driver's behavior in order to alert him/her in case of drowsiness states to avoid potential accidents. The main contribution of the proposed approach is to build a reliable system able to avoid false detected drowsiness situations and to alert only the real ones. To this end, our research procedure is divided into two processes. The offline process performs a classification module using pretrained Convolutional Neural Networks (CNNs) to detect the drowsiness of the driver. In the online process, we calculate the percentage of the eyes' closure and yawning frequency of the driver online from real-time video using the Chebyshev distance instead of the classic Euclidean distance. The accurate drowsiness state of the driver is evaluated with the aid of the pretrained CNNs based on an ensemble learning paradigm. In order to improve models' performances, we applied data augmentation techniques for the generated dataset. The accuracies achieved are 97 % for the VGG16 model, 96% for VGG19 model and 98% for ResNet50 model. This system can assess the driver's dynamics with a precision rate of 98%

A comparison of reading on computer screens and print media: measurement of attention patterns using EEG

This study explores if media affect attention patterns for subjects reading text. A series of typography studies done in the mid-1990\u27s show that typography for the computer screen does differ from print standards. A further review of the literature suggests that there is a difference in how the brain reacts to various media. Some of these variations indicate a decrease in attention and ability to concentrate. However, no studies have been done to confirm that the task of reading varies significantly dependent on media, nor has the research studied brain patterns of subjects\u27 attention to reading materials using various media.;This exploratory study measuring the EEG of 15 female subjects indicates that 60% of the subject showed greater attention to reflective print media, 20% to CRT computer screens and the remaining subjects showed mixed reactions. A method is provided to statistically test these differences. Statistically significant differences appear in the information processing in the parietal lobes when comparing attention of readers using CRT and print and CRT screen and LCD screens. It appears that the flicker from the screen, while not consciously noticed, may be the cause of these differences.;If attention varies because of the medium, it has significant implications for distance education, the media and for industry in general. It will help professionals understand strengths and weaknesses of different media and to use each appropriately for delivery of information

On Motion Analysis in Computer Vision with Deep Learning: Selected Case Studies

Motion analysis is one of the essential enabling technologies in computer vision. Despite recent significant advances, image-based motion analysis remains a very challenging problem. This challenge arises because the motion features are extracted directory from a sequence of images without any other meta data information. Extracting motion information (features) is inherently more difficult than in other computer vision disciplines. In a traditional approach, the motion analysis is often formulated as an optimisation problem, with the motion model being hand-crafted to reflect our understanding of the problem domain. The critical element of these traditional methods is a prior assumption about the model of motion believed to represent a specific problem. Data analytics’ recent trend is to replace hand-crafted prior assumptions with a model learned directly from observational data with no, or very limited, prior assumptions about that model. Although known for a long time, these approaches, based on machine learning, have been shown competitive only very recently due to advances in the so-called deep learning methodologies. This work's key aim has been to investigate novel approaches, utilising the deep learning methodologies, for motion analysis where the motion model is learned directly from observed data. These new approaches have focused on investigating the deep network architectures suitable for the effective extraction of spatiotemporal information. Due to the estimated motion parameters' volume and structure, it is frequently difficult or even impossible to obtain relevant ground truth data. Missing ground truth leads to choose the unsupervised learning methodologies which is usually represents challenging choice to utilize in already challenging high dimensional motion representation of the image sequence. The main challenge with unsupervised learning is to evaluate if the algorithm can learn the data model directly from the data only without any prior knowledge presented to the deep learning model during In this project, an emphasis has been put on the unsupervised learning approaches. Owning to a broad spectrum of computer vision problems and applications related to motion analysis, the research reported in the thesis has focused on three specific motion analysis challenges and corresponding practical case studies. These include motion detection and recognition, as well as 2D and 3D motion field estimation. Eyeblinks quantification has been used as a case study for the motion detection and recognition problem. The approach proposed for this problem consists of a novel network architecture processing weakly corresponded images in an action completion regime with learned spatiotemporal image features fused using cascaded recurrent networks. The stereo-vision disparity estimation task has been selected as a case study for the 2D motion field estimation problem. The proposed method directly estimates occlusion maps using novel convolutional neural network architecture that is trained with a custom-designed loss function in an unsupervised manner. The volumetric data registration task has been chosen as a case study for the 3D motion field estimation problem. The proposed solution is based on the 3D CNN, with a novel architecture featuring a Generative Adversarial Network used during training to improve network performance for unseen data. All the proposed networks demonstrated a state-of-the-art performance compared to other corresponding methods reported in the literature on a number of assessment metrics. In particular, the proposed architecture for 3D motion field estimation has shown to outperform the previously reported manual expert-guided registration methodology

The pupil in glaucoma

Glaucoma is the most common preventable cause of blind-registration in elderly Western populations. Case-finding is crucial for the prevention of blindness. There is no single test that can reliably diagnose glaucoma, especially early cases. The relative afferent pupillary detect (RAPD) is known to be sensitive in the detection of optic nerve pathology. The clinical swinging flash light test is well used for this purpose. However, the test requires skill and careful interpretation, and the sensitivity of the test is limited to ≥0.3 log units of relative pupillomotor deficit. Some of the newly-built commercially available pupillometers measure the pupil parameters with accuracy. These instruments have mainly been used in the area of refractive surgery. This thesis considers the applicability of the commercially available pupillometer P3000 to the diagnosis of glaucoma. In this thesis a pupillometer (P3000) was calibrated before the stimulus parameters were tested for their best suitability for the RAPD test. The stimulus and outcome parameters were optimised. The chosen stimulus configuration (0.4s-1.6s on-off combination) produced repeatable results. The eyes were dark adapted only for 30 seconds before each test sequence for practical use in clinics. The pupillographic RAPD was calculated from the pupil constriction amplitudes calibrated in response to 3 levels of light stimulus. Data was collected on normal and glaucomatous subjects. There was no significant diurnal variation in the RAPD noted for both cohorts and the immediate repeatability was high. The final test was used in a methods comparison study to detect glaucoma against the gold standard of clinical diagnosis. The area under the Receiver Operating Characteristic curve for the detection of all grades of unilateral or bilateral glaucoma was in the region of 0.81 for the cohort of 101 normal and 117 glaucoma patients. Pupillometry may be helpful as an adjunctive test in the detection of glaucoma

International Journal of Medical Students - Year 2015 - Volume 3 - Supplement 1

International Journal of Medical Students - Year 2015 - Volume 3 - Supplement

Aerospace Medicine and Biology. an Annotated Bibliography. 1958-1961 Literature, Volumes VII-X, Part 2

Abstracts on aerospace medicine and biology - bibliography on environmental factors, safety and survival, personnel, pharmacology, toxicology, and life support system

History of Psychology

Openly licensed anthology focused on the theme of the History of Psychology. Contains: The Mind and the Brain by Alfred Binet; Dream Psychology: Psychoanalysis for Beginners by Sigmund Freud; The Principles of Psychology, Volume 1 (of 2) by William James; The Principles of Psychology, Volume 2 (of 2) by William James; Collected Papers on Analytical Psychology by C. G. Jung; Memoirs of Extraordinary Popular Delusions and the Madness of Crowds by Charles Mackay; The Psychology of Arithmetic by Edward L. Thorndike