Deep learning for healthcare applications based on physiological signals: A review.

Driver fatigue has been attributed to traffic accidents; therefore, fatigue-related traffic accidents have a higher fatality rate and cause more damage to the surroundings compared with accidents where the drivers are alert. Recently, many automobile companies have installed driver assistance technologies in vehicles for driver assistance. Third party companies are also manufacturing fatigue detection devices; however, much research is still required for improvement. In the field of driver fatigue detection, continuous research is being performed and several articles propose promising results in constrained environments, still much progress is required. This paper presents state-of-the-art review of recent advancement in the field of driver fatigue detection. Methods are categorized into five groups, i.e., subjective reporting, driver biological features, driver physical features, vehicular features while driving, and hybrid features depending on the features used for driver fatigue detection. Various approaches have been compared for fatigue detection, and areas open for improvements are deduced.

Driver Fatigue Detection Systems: A Review

Transportation Research Part F: Traffic Psychology and Behaviour

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class-balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages.

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks.

Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

Deep Learning in the Biomedical Applications: Recent and Future Status

This study provides a new application of convolutional neural networks for drowsiness detection based on electrooculography (EOG) signals. Drowsiness is charged to be one of the major causes of traffic accidents. Such application is helpful to reduce losses of casualty and property. Most attempts at drowsiness detection based on EOG involve a feature extraction step, which is accounted as time-consuming task, and it is difficult to extract effective features. In this paper, an unsupervised learning is proposed to estimate driver fatigue based on EOG. A convolutional neural network with a linear regression layer is applied to EOG signals in order to avoid using of manual features. With a postprocessing step of linear dynamic system (LDS), we are able to capture the physiological status shifting. The performance of the proposed model is evaluated by the correlation coefficients between the final outputs and the local error rates of the subjects. Compared with the results of a manual ad-hoc feature extraction approach, our method is proven to be effective for drowsiness detection.

/pdf/eog-based-drowsiness-detection-using-convolutional-neural-1g0kyjzelj.pdf

EOG-based drowsiness detection using convolutional neural networks

The invention provides a device monitoring suspension of stimulated animal weightlessness tails in real time. The device comprises at least one box body unit, the box body unit comprises at least one tail suspending cage, a camera shooting device and a computer system, wherein the tail suspending cage is arranged in the box body unit and used for providing space for a suspension test on the animal tails; the camera shooting device is arranged above the box body unit and used for acquiring image information of the suspension test on the animal tails in the box body unit, a suspending device is arranged above each tail suspending cage and used for being connected with the animal tails, each tail suspending cage is provided with a falling-off detecting device which is used for detecting whether the animal tails fall off in the suspension test on the animal tails, and the falling-off detecting device gives out an alarming signal when the animal tails are detected to fall off; the computer system is used for receiving the image information transmitted by the camera shooting device and the alarming signal transmitted by the falling-off detecting device and transmitting the alarming signal to an appointed tester. The device can accurately acquire the state of autonomic action of the suspended animal tails and automatically give an alarm when the animal tails fall off.

Device monitoring suspension of stimulated animal weightlessness tails in real time

Slow eye movement (SEM) regarded as a sign of onset of sleep is very significant for detecting driver fatigue, but its characteristics and detection algorithm have been rarely involved in the study of driver fatigue detection. In this study, some new features were extracted based on wavelet singularity analysis and statistics to detect SEMs. Six subjects participated in this simulated driving experiment, and for each subject, a more than 2 hours electro-oculogram (EOG) session was recorded. Each session was divided into SEM epochs and non-SEM epochs according to the common judgments made by the two of three experts by the visual recognition criteria of SEMs. Regarding the problem of detecting SEMs as an imbalance classification problem, and through the under-sampling and over-sampling methods a 2s horizontal electro-oculogram (HEO) signal could finally be recognized as the category of SEMs or non-SEMs with the classifiers SVM, GELM, and KNN respectively. Results prove that the proposed features was a little better than the wavelet energy features, and through the combination of the wavelet energy features and the new features based on wavelet singularity analysis and statistics, the classification results were improved obviously.

Recognizing Slow Eye Movement for Driver Fatigue Detection with Machine Learning Approach

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaoping Chen

Author Tools

Chat about Author

Papers

EOG-based drowsiness detection using convolutional neural networks

Device monitoring suspension of stimulated animal weightlessness tails in real time

Recognizing Slow Eye Movement for Driver Fatigue Detection with Machine Learning Approach