Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection

This work was supported by National Natural Science Foundation of China under Grant Nos. 61473203, 61873181 and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.Peer reviewedPostprin

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of certain types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. Different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or the brain activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Further, potential topics in relation to driving behaviour are identified that could benefit from the adoption of neuroimaging methods in future studies

Cognitive and physical effort of surgeons using master/slave surgical systems for minimally invasive surgery

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2020The integration of technology in health care has been increasing over the years, allowing more and better diagnoses and treatments in various areas of health. One of these areas is the surgery area, with da Vinci system being one of the most successful and most commercialized. Despite all the advantages, there are some disadvantages such as a limited number of degrees of freedom, the possibility of collision between the different robotic arms, among others. Therefore, the SMARTsurg (Smart Wearble Robotic Teleoperated Surgery) project was developed for the purpose of correcting these problems, proposing a wearable robotic system for minimally invasive surgeries, offering surgeons more natural movements. However, to verify that this is a necessary improvement, it is necessary to understand how these changes affects the performance and condition of surgeons. Thus, the aim of this dissertation is to compare the muscular and mental effort of surgeons when performing tasks using the da Vinci system and the SMARTsurg system. Biological signals such as electromyography (EMG) and electroencephalography (EEG) were studied to verify how these signals changed using each of the systems. Using these biosignals it was possible to evaluate muscle and mental fatigue in the participants of this study. The experimental trials with the da Vinci system took place at Southmead Hospital Bristol, where the fatigue of clinicians was tested, performing a series of surgical training tasks in the simulator embedded in the da Vinci systems. For the SMARTsurg system, the trials took place at the Bristol Robotics Lab, having been tested volunteers from the lab without any experience in robotic surgical systems and one of the subjects who participated in the trial in the hospital. Also here, subjects were asked to perform small surgical training tasks, similar to those of the da Vinci system. The analysis of the results showed that it was only possible to verify muscle fatigue in participants using the da Vinci system, and it was not possible to determine any type of mental fatigue using any of the systems. Nevertheless, it will be necessary to do more experiments to verify a broader trend in the data, and it was possible to determine with this dissertation the validity of the use of two wireless devices available in the market to infer conclusions about human physiological changes