1,061 research outputs found

    EEG-based driver fatigue detection using hybrid deep generic model

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    © 2016 IEEE. Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG

    Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection

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    This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.Comment: 10 Pages, 4 figure

    Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

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    Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

    Fatigue detection system to aid in remote work

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    During the Covid-19 pandemic there was a noticeable surge in the amount of remote workers. In the aftermath of the pandemic working from home still remains a reality for many workers with noticeable impacts on the mental health of people. With the increased stress caused by current situation and the harder time establishing boundaries there was an increase in the overall stress and fatigue in workers, leading to burnouts. Fatigue detection systems are used in several areas, mainly in the automotive industry as a mean to decrease the number of accidents. This research started by approaching the Artificial Intelligence (AI) area and its domains, followed by a study of the current techniques used in order to predict fatigue. With the main ones utilising eye state, facial landmarks, electrocardiogram or heart rate. After a research into existing Fatigue detection systems was done in order to identify the strengths of solutions currently in the market, whether in the automotive industry or other applications. This thesis proposes the creation of a system able to detect fatigue in a user as well as warn him when fatigue levels increase. This system incorporates a webcam analysing the users face and performing eye state detection in order to calculate the percentage of the time the eyes are closed (PERCLOS). Heart rate data was also analysed and a model was developed in order to incorporate this data, the percentage of time the eyes are closed, the program the user has open and time of day in order to predict the level of fatigue. By combining these two different techniques this system can be more effective and more accurate in giving predictions of the level of fatigue. The review of literature showed that the conjunction of these two techniques in predicting fatigue is novelty. The developed system also contains integration with smartwatch technology in order to both harness heart rate data as well as communicate with the user via pop up notifications to inform him when fatigue levels get too high. The conclusion of this work is that eye state detection using Artificial Intelligence can achieve a high accuracy and be a reliable tool in identifying fatigue in an user. The combination of Heart Rate and PERCLOS allows the system to have a higher accuracy as well as not being completely reliant on one sensor. The creation of a fatigue prediction model was hindered by the lack of existent data in order to train a model, a problem that could be fixed with the adoption of the system in a broader scope.Durante a pandemia de Covid-19, houve um aumento notável na quantidade de trabalhadores remotos. No rescaldo da pandemia, trabalhar a partir de casa continua a ser uma realidade para muitos trabalhadores, com impactos visíveis na saúde mental das pessoas. Com o aumento do stresse causado pela situação atual e a dificuldade de estabelecer limites, houve um aumento do stresse geral e da fadiga dos trabalhadores, levando ao esgotamento. Os sistemas de detecção de fadiga são utilizados em diversas áreas, principalmente na indústria automobilística como forma de diminuir o número de acidentes. Este estudo começou por abordar a área de Inteligência Artificial (IA) e os seus domínios, seguida de um estudo das técnicas atuais utilizadas para prever a fadiga. Com os principais utilizando o estado dos olhos, pontos de referência faciais, eletrocardiograma ou frequência cardíaca. Depois foi feita uma pesquisa sobre os sistemas de detecção de fadiga existentes de forma a identificar os pontos fortes das soluções actualmente no mercado, quer seja na indústria automóvel ou outras aplicações. Esta dissertação propõe a criação de um sistema capaz de detectar fadiga num utilizador, bem como alertar quando os níveis de fadiga aumentam. Este sistema incorpora uma webcam que analisa a face do utilizador e realiza a detecção do estado dos olhos para calcular a percentagem de tempo em que os olhos estão fechados (PERCLOS). Os dados de frequência cardíaca também foram analisados e um modelo foi desenvolvido para incorporar estes dados, a percentagem de tempo que os olhos ficam fechados, o programa que o utilizador tem aberto e a hora do dia para prever o nível de fadiga. Ao combinar essas duas técnicas diferentes, este sistema pode ser mais eficaz e mais preciso em fornecer previsões do nível de fadiga. A revisão da literatura mostrou que a conjunção dessas duas técnicas na previsão da fadiga é novidade. O sistema desenvolvido também contém integração com a tecnologia smartwatch para aproveitar os dados da frequência cardíaca e comunicar com o utilizador por meio de notificações pop-up para informá-lo quando os níveis de fadiga se encontrarem altos. A conclusão deste trabalho é que a detecção do estado ocular usando Inteligência Artificial pode alcançar uma alta precisão e ser uma ferramenta confiável na identificação de fadiga num utilizador. A combinação da frequência cardíaca e PERCLOS permite que o sistema tenha maior precisão, além de não depender completamente de um unico sensor. A criação de um modelo de previsão de fadiga foi dificultada pela falta de dados existentes para treinar um modelo, problema que poderia ser colmatado com a adoção do sistema numa população maior

    A novel Big Data analytics and intelligent technique to predict driver's intent

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    Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

    Past, Present, and Future of EEG-Based BCI Applications

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    An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

    Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

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    Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data
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