Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.

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A Survey on State-of-the-Art Drowsiness Detection Techniques

In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

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Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ~27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection.

The most common treatment for hydrocephalus is placement of a cerebrospinal fluid shunt to supplement or replace lost drainage capacity. Shunts are life-saving devices but are notorious for high failure rates, difficulty of diagnosing failure, and limited control options. Shunt designs have changed little since their introduction in 1950s, and the few changes introduced have had little to no impact on these long-standing problems. For decades, the community has envisioned a “smart shunt” that could provide advanced control, diagnostics, and communication based on implanted sensors, feedback control, and telemetry. The most emphasized contribution of smart shunts is the potential for advanced control algorithms, such as weaning from shunt dependency and personalized control. With sensor-based control comes the opportunity to provide data to the physician on patient condition and shunt function, perhaps even by a smart phone. An often ignored but highly valuable contribution would be designs that correct the high failure rates of existing shunts. Despite the long history and increasing development activity in the past decade, patients are yet to see a commercialized smart shunt. Most smart shunt development focuses on concepts or on isolated technical features, but successful smart shunt designs will be a balance between technical feasibility, economic viability, and acceptable regulatory risk. Here, we present the status of this effort and a framework for understanding the challenges and opportunities that will guide introduction of smart shunts into patient care.

New and improved ways to treat hydrocephalus: Pursuit of a smart shunt.

Brain-Computer  Interface  (BCI)  enables  users  to interact with  a  computer  only  through  their  brain  biological signals,  without  the  need to  use  muscles.  BCI  is  an emerging research  area  but  it  is  still  relatively  immature. However,  it  is important  to  reflect  on  the  different aspects of  the  Human-Computer  Interaction  (HCI)  area  related to BCIs,  considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience  by  using  brain  signals  as  input  with  immersive environments  as  output.  This  paper  addresses  only  noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

/pdf/a-survey-of-interactive-systems-based-on-brain-computer-3w32owqpgy.pdf

A Survey of Interactive Systems based on Brain- Computer Interfaces

Fatigue is a gradual process leads to a slower reaction time. Fatigue is the major cause of road accidents around the globe. This paper proposes, implements and tests a system to detect fatigue based on Electroencephalogram (EEG) signal. The system produces fatigue index which is relevant to the level of subject's drowsiness. The input to the system is EEG signal which is measured by inexpensive single electrode neuro-signal acquisition device. The system was tested on locally collected dataset for a car simulation driver in different drowsiness levels. The system was able to detect the fatigue level for all subjects in different levels of tiredness.

EEG-based Driver Fatigue Detection

Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis.

https://www.mdpi.com/2073-4425/8/11/326/pdf

Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

The idea of this paper is to implement an efficient block-by-block singular value (SV) decomposition digital image watermarking algorithm, which is implemented in both the spatial and transforms domains. The discrete wavelet transform (DWT), the discrete cosine transform and the discrete Fourier transform are exploited for this purpose. The original image or one of its transforms is segmented into non-overlapping blocks, and consequently the image to be inserted as a watermark is embedded in the SVs of these blocks. Embedding the watermark on a block-by-block manner ensures security and robustness to attacks such like Gaussian noise, cropping and compression. The proposed algorithm can also be used for colour image watermarking. A comparison study between the proposed block-based watermarking algorithm and the method of Liu is performed for watermarking in all domains. Simulation results ensure that the proposed algorithm is more effective than the traditional method of Liu, especially when the watermarki...

Block-based SVD image watermarking in spatial and transform domains

Brain Computer Interface (BCI) introduces a new communication system that does not depend on brain's normal output pathways. This paper studies the feasibility of using inexpensive Electroencephalogram(EEG) device for BCI with asynchronous BCI mode which leads the control mechanism to become highly available for variety of users and a more natural way to communicate than the current BCI versions which depend on expensive EEG devices, which are less practical where they need special environment to run the BCI system, and use synchronous BCI mode in most cases. Benchmarking the results using Emotiv to another complex EEG device (BrainAmp) shows the reliability of such inexpensive devices.

Toward Inexpensive and Practical Brain Computer Interface

One of the crucial and inherent issues in a practical iris recognition system is the occlusion that happens due to eyelids and eyelashes. This occlusion increases the complexity and degrades the performance of matching and feature extraction processes. Generally, two types of approaches have been proposed to solve this issue. The first approach requires generating an iris mask that indicates which part of the iris is useful and which others are occluded. However, in the second approach, a fixed region of interest (ROI) within the iris area is selected to avoid the regions of occlusion. In this paper, we experimentally study both approaches but due to the latter characteristic, which is its ability to simplify the matching and feature extraction processes, it has been adopted in our techniques used, specifically for iris segmentation, iris normalization, and feature extraction. Accordingly, for matching and feature extraction, the lower side of the pupillary region (i.e. the innermost 25% of the lower half of the iris ring) is found to be the best ROI. This small area of iris is almost free of eyelids and eyelashes and it contains abundant texture information. Interestingly, this selection of small area helps us in proposing a simple yet efficient technique for feature extraction, called mean-based feature extraction technique (MB-FET). This technique is based on analyzing the local intensity variations. The proposed technique achieves a lower processing burden than other traditional methods such as Fourier or wavelet decompositions (e.g. Gabor wavelet). In most traditional techniques, many parameters (e.g. five parameters for 2D-Gabor filter) must be optimally determined in advance to achieve an accurate feature extraction process. Unfortunately, these parameters may not match various variations in image capturing conditions (e.g. variations in illumination due to change in image capturing distance). Moreover, the basic functions of the traditional methods are fixed in advance (off-line) and do not necessarily match the texture of all irises in the database. However, for our proposed technique MB-FET, there is no need to determine in advance any parameter or basic function. MB-FET dynamically adapts its parameter (only one parameter) with intensity variations. The proposed technique generates a binary iris code, hence a simple and fast matching process is done using the Hamming distance. The experimental results using the CASIA iris database show that the proposed technique achieves promising results for a robust and reliable iris recognition.

Nayel Al-Zubi

Papers

EEG-based Driver Fatigue Detection

Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

Block-based SVD image watermarking in spatial and transform domains

Toward Inexpensive and Practical Brain Computer Interface

Effect of Eyelid and Eyelash Occlusions on a Practical Iris Recognition System: Analysis and Solution