Implementation of a psychomotor vigilance test to investigate the effects of driving fatigue on oil and gas truck drivers’ performance

IntroductionDriving fatigue has been shown to increase the risk of accidents and potentially fatal crashes. Fatigue is a serious risk that some drivers do not take seriously. Previous studies investigated the effects of driving fatigue in the Malaysian oil and gas transportation industry by employing survey questionnaires. However, they did not explain the behavior of fatigue. Besides, these results required validation by a more reliable method that can describe how fatigue occurs.MethodsThus, in this study, we used the Psychomotor Vigilance Test (PVT-192) and a short survey to address driving fatigue behavior and identify the influences of driving fatigue on driving performance in real life (on the road) with actual oil and gas tanker drivers. The total participants in the experimental study were 58 drivers.ResultsFor the analysis, a Wilcoxon Signed Ranks Test, Z value and Spearman’s rho were used to measure the significant difference between the pre and post-tests of PVT and the correlation between the fatigue variables and driving performance.DiscussionDuring the experiment’s first and second days, this study’s results indicated that driving fatigue gradually escalated. Likewise, there was a negative correlation based on the test of the relationship between the PVT data and the driving performance survey data. Additionally, the drivers suffer from accumulative fatigue, which requires more effort from the transportation company management to promote the drivers awareness of fatigue consequences

Modelling and control of standing up and sitting down manoeuver

Exoskeleton Robot is one of the most significant examples of human-oriented robotic devices. Nevertheless, the main challenge remains the complexity of their mechanical design and human-robot interfaces. This paper is an outcome of a research to model and to simulate the support of mobility of an elderly people using exoskeleton. Exoskeleton is developed in order to complement the corporal deficiencies of an elderly person in standing up and sitting down. When the natural joint torques is integrated with the exoskeleton's torque the result is in an overall torque that is comparable to that of a physically normal person. This work focuses on standing-up and sitting-down movements. Appropriate simulation models are formulated and their performances examined against measured data. The results with PID control show that at different speed of standing up and sitting down, the joint torques can be compromised. This is done within allowable limits

Study of the Acute Stress Effects on Decision Making Using Electroencephalography and Functional Near-Infrared Spectroscopy: A Systematic Review

This systematic review provides a comprehensive analysis of studies that use electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to investigate how acute stress affects decision-making processes. The primary goal of this systematic review was to examine the influence of acute stress on decision making in challenging or stressful situations. Furthermore, we aimed to identify the specific brain regions affected by acute stress and explore the feature extraction and classification methods employed to enhance the detection of decision making under pressure. Five academic databases were carefully searched and 27 papers that satisfied the inclusion criteria were found. Overall, the results indicate the potential utility of EEG and fNIRS as techniques for identifying acute stress during decision-making and for gaining knowledge about the brain mechanisms underlying stress reactions. However, the varied methods employed in these studies and the small sample sizes highlight the need for additional studies to develop more standardized approaches for acute stress effects in decision-making tasks. The implications of the findings for the development of stress induction and technology in the decision-making process are also explained

Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications

Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study

Effect of Interruptions and Cognitive Demand on Mental Workload: A Critical Review

Worker safety and productivity are crucial for effective job management. Interruptions to an individual’s work environment and their impact on mental health can have adverse effects. One prospective instrument for assessing and calculating an individual’s mental state in an interrupted scenario and cognitive demand levels is the use of physiological computing devices in conjunction with behavioral and subjective measurements. This study sought to address how to gather and compute data on individuals’ cognitive states in interrupted work settings through critical analysis. Thirty-three papers were considered after the literature search and selection procedure. This descriptive study is conducted from three perspectives: parameter measurement, research design, and data analysis. The variables evaluated were working memory, stress, emotional state, performance, and resumption lag. The subject recruitment, experimental task design, and measurement techniques were examined from the standpoint of the experimental design. Data analysis included computing and cognitive pre-processing. Four future research directions are suggested to address the shortcomings of the present studies. This study offers suggestions for researchers on experiment planning and using computing to analyze individuals’ cognitive states during interrupted work scenarios. Additionally, it offers helpful recommendations for organizing and conducting future research

Hemodynamic Response Asymmetry of the Prefrontal Cortex During a Cognitive Load Task

Investigations of the prefrontal cortex (PFC) asymmetry have been conducted in neuroscience research during cognitive load using +electroencephalography (EEG) and other neuroimaging techniques. A few studies used functional near-infrared signals (fNIRS) to analyze asymmetry during cognitive load. This study examined the hemodynamic response asymmetry in the PFC area during N-back load memory tasks, including ive, 2-, and 3-back electroencephalography (EEG) and other neuroimaging techniques. A few studies used functional near-infrared signals (fNIRS) to analyze asymmetry during cognitive load. This study examined the hemodynamic response asymmetry in the PFC area during N-back load memory tasks, including 1-, 2-, and 3-back. The investigation results show that the asymmetry index value fluctuates as the level of memory load rises. In particular, the 1-back task's positive asymmetry index value (M = 0.2761,SD = 0.4139) suggested that left-hemisphere activity was more remarkable than right-hemisphere activation. The asymmetry index, on the other hand, revealed a negative value of (M = - 0.2105,SD= 0.4252) and (M = - 0.3665,SD = 1.2472) for the 2-back and 3-back memory tasks, respectively, indicating that the right hemisphere experienced a more significant increase in Hbo activation than the left.YUTP-FRG through PRF under grant number 015LC0-35

Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder

The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of the FC of brain nodes. Hence, methods based on linear correlations of rs-fMRI may not accurately represent the FC patterns of brain nodes in ASD patients. In this study, we proposed a new biomarker for ASD detection based on wavelet coherence and singular value decomposition. In essence, the proposed method provides a novel feature-vector based on extraction of the principal component of the neuronal dynamic FC patterns of rs-fMRI BOLD signals. The method, known as principal wavelet coherence (PWC), is implemented by applying singular value decomposition (SVD) on wavelet coherence (WC) and extracting the first principal component. ASD biomarkers are selected by analyzing the relationship between ASD severity scores and the amplitude of wavelet coherence fluctuation (WCF). The experimental rs-fMRI dataset is obtained from the publicly available Autism Brain Image Data Exchange (ABIDE), and includes 505 ASD patients and 530 normal control subjects. The data are randomly divided into 90% for training and cross-validation and the remaining 10% unseen data used for testing the performance of the trained network. With 95.2% accuracy on the ABIDE database, our ASD classification technique has better performance than previous methods. The results of this study illustrate the potential of PWC in representing FC dynamics between brain nodes and opens up possibilities for its clinical application in diagnosis of other neuropsychiatric disorders

Eeg-based control for upper and lower limb exoskeletons and prostheses: A systematic review

Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research