Search Results (878)

The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, “Human Fatigue Assessment Based on Video Data” (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue. Full article

Automotive headlights are crucial for nighttime driving, but accidents frequently occur when drivers fail to dim their high beams in the presence of oncoming vehicles, causing temporary blindness and increasing the risk of collisions. To address this problem, the current study developed an intelligent headlight system using a sensor-based approach to control headlight beam intensity. This system is designed to distinguish between various light sources, including streetlights, building lights, and moving vehicle lights. The primary goal of the study was to create an affordable alternative to machine-learning-based intelligent headlight systems, which are limited to high-end vehicles due to the high cost of their components. In simulations, the proposed system achieved a 98% success rate, showing enhanced responsiveness, particularly when detecting an approaching vehicle at 90°. The system’s effectiveness was further validated through real-vehicle implementation, confirming the feasibility of the approach. By automating headlight control, the system reduces driver fatigue, enhances safety, and minimizes nighttime highway accidents, contributing to a safer driving environment. Full article

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers’ cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the “fatigued” spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery). Full article

Offshore wind turbine (WT) wake interference will reduce power generation and increase the fatigue loads of downstream WTs. Wake interference detection based on aeroacoustic noise is believed to solve these challenges in offshore wind farms. However, aeroacoustic noise is closely related to the aerodynamics around WT blades, and the acoustic detection method requires the mastery of noise emission characteristics. In this paper, FAST.Farm, combined with the acoustic model in OpenFAST, is utilized to investigate the acoustic noise emission characteristics from two 3.4 MW-130 WTs with wake interference. Multi-microphone positions were investigated for the optimal reception selection under 8 m/s and 12 m/s wind speeds with a typical offshore atmospheric turbulence intensity of 6%. The numerical simulation results indicate that wake deficit reduces the total noise emission by about 6 dBA in the overall sound pressure level (OASPL) at 8 m/s, while wake turbulence marginally increases it and its fluctuation. There is a mutual influence between these effects, and the wake deficit effect can be 100% compensated for in the OASPL at 12 m/s. Additionally, downstream observer locations are suggested based on comparisons. These investigations provide new insights into wake interference in offshore wind farms. Full article

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala’s heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. Full article

This paper reviews recent biomechanical studies on heart valve flutter. The function of the heart valves is essential for maintaining effective blood circulation. Heart valve flutter is a kind of small vibration phenomenon like a flag fluttering in the wind, which is related to many factors such as a thrombus, valve calcification, regurgitation, and hemolysis and material fatigue. This vibration phenomenon is particularly prevalent in valve replacement patients. The biomechanical implications of flutter are profound and can lead to micro-trauma of valve tissue, accelerating its degeneration process and increasing the risk of thrombosis. We conducted a systematic review along with a critical appraisal of published studies on heart valve flutter. In this review, we summarize and analyze the existing literature; discuss the detection methods of frequency and amplitude of heart valve flutter, and its potential effects on valve function, such as thrombosis and valve degeneration; and discuss some possible ways to avoid flutter. These findings are important for optimizing valve design, diagnosing diseases, and developing treatment strategies. Full article

The large cutout structure is a key component in the bottom skin of an airplane wing, and is susceptible to developing fatigue cracks under service loads. Early fatigue crack detection is crucial to ensure structural safety and reduce maintenance costs. Nonlinear Lamb wave techniques show significant potential in microcrack monitoring. However, nonlinear components are often relatively weak. In addition, a large cutout structure introduces complex boundary conditions for Lamb wave propagation, making nonlinear Lamb wave monitoring more challenging. This article proposes an integrated data processing method, combining phase inversion with continuous wavelet transform (CWT) to enhance crack detection in complex structures, with phase-velocity desynchronization adopted to suppress the material nonlinearity. Experiments on a large cutout aluminum alloy plate with thickness variations were conducted to validate the proposed method, and the results demonstrated its effectiveness in detecting fatigue cracks. Furthermore, this study found that nonlinear components are more effective than linear components in monitoring closed cracks. Full article

A 67-year-old man started treatment due to frequent asymptomatic premature ventricular complexes (PVCs) accidentally being registered during a preventive examination by a specialist, because of which he was referred to cardiologist. During the initial 24-hour (h) ECG monitoring, 4.5% PVCs and one episode of asymptomatic non-sustained ventricular tachycardia (NSVT) with three PVCs in row, at a frequency of 150 beats per minute (bpm), were detected. After the introduction of beta blockers into therapy, a lower number of PVCs, without NSVT, were recorded in the control 24 h Holter ECG, while transthoracic echocardiography (TTE) showed normal left ventricular (LV) systolic function without cardiomyopathy. So, an exercise test was indicated, and it was interrupted in the third minute at 120 beats per minute (bpm) due to fatigue and pain in the hips, without malignant arrhythmias, angina or dyspneic complaints. During the rest period, a significant inferolateral depression of the ST junction was observed, which recovered in the ninth minute. Immediately after the ECG monitoring stopped, the patient lost consciousness; his pulse was not palpable, but breathing was audible, so cardiac massage was started. After he had regained consciousness, the ECG showed alternating sinus and junctional rhythm with the lowest frequency of 33 bpm, which was accompanied by marked hypotension (80/50 mmHg). The patient was immediately hospitalized; coronary angiography and repeated TTE were completely normal, while continuous ECG monitoring did not confirm malignant rhythm disorders or asystole. It was concluded that it was vasovagal syncope (VVS), most likely caused by the Bezold–Jarisch reflex (BJR). Full article

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components. Full article

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method. Full article

Vortex-induced vibration is a type of wind-induced vibration occurring frequently in large-span sea-crossing bridges under relatively low wind speeds, posing a threat to the structural fatigue performance and driving comfort. Identifying the instantaneous occurrence moments of vortex-induced vibration is a prerequisite for establishing a data-driven prediction model for vortex-induced vibration, and it is of great significance for the monitoring and early warning of vortex-induced vibration performance in bridges. To automatically detect the occurrence moments of vortex-induced vibration and establish a correlation model between vortex-induced vibration amplitude and environmental factors, this study proposes a fuzzy C-means clustering-based classification method. In order to detect the occurrence moments of vortex-induced vibration more finely, only short-term or even instantaneous structural vibration indicators were selected and transformed for distribution as clustering features. The entire detection process could be carried out unsupervised, reducing the manual cost of obtaining vortex-induced vibration information from massive monitoring data. Finally, actual vortex-induced vibration test data from a certain overseas bridge was utilized to verify the feasibility of this method. Based on the classification results, the correlation between vortex-induced vibration amplitude and environmental variables was determined, providing valuable guidance for predicting vortex-induced vibration amplitudes. Full article

Ferroptosis, a lipid peroxidation- and iron-mediated type of regulated cell death, relates to both neuroinflammation, which is common in relapsing-remitting multiple sclerosis (RRMS), and neurodegeneration, which is prevalent in progressive (P)MS. Currently, findings related to the molecular markers proposed in this paper in patients are scarce. We analyzed circulatory molecular indicators of the main ferroptosis-related processes, comprising lipid peroxidation (malondialdehyde (MDA), 4-hydroxynonenal (4-HNE), and hexanoyl–lysine adduct (HEL)), glutathione-related antioxidant defense (total glutathione (reduced (GSH) and oxidized (GSSG)) and glutathione peroxidase 4 (GPX4)), and iron metabolism (iron, transferrin and ferritin) to estimate their contributions to the clinical manifestation of MS and differences between RRMS and PMS disease course. In 153 patients with RRMS and 69 with PMS, plasma/serum lipid peroxidation indicators and glutathione were quantified using ELISA and colorimetric reactions, respectively. Iron serum concentrations were determined using spectrophotometry, and transferrin and ferritin were determined using immunoturbidimetry. Compared to those with RRMS, patients with PMS had decreased 4-HNE (median, 1368.42 vs. 1580.17 pg/mL; p = 0.03). Interactive effects of MS course (RRMS/PMS) and disease-modifying therapy status on MDA (p = 0.009) and HEL (p = 0.02) levels were detected. In addition, the interaction of disease course and self-reported fatigue revealed significant impacts on 4-HNE levels (p = 0.01) and the GSH/GSSG ratio (p = 0.04). The results also show an association of MS course (p = 0.03) and EDSS (p = 0.04) with GSH levels. No significant changes were observed in the serum concentrations of iron metabolism indicators between the two patient groups (p > 0.05). We suggest circulatory 4-HNE as an important parameter related to differences between RRMS and PMS. Significant interactions of MS course and other clinically relevant parameters with changes in redox processes associated with ferroptosis support the further investigation of MS with a larger sample while taking into account both circulatory and central nervous system estimation. Full article

Fibromyalgia (FM) is a chronic disorder characterized by widespread musculoskeletal pain often accompanied by fatigue, sleep disturbances, memory issues, and mood disorders. The exact cause of FM remains unknown, and diagnosis is typically based on a history of persistent widespread pain, as there are no objective biomarkers usable in diagnosis of this disorder available. The aim of this study was to identify measurable indicators specific to FM with potential as biomarkers. This study included 17 individuals diagnosed with FM and 24 apparently healthy persons. Using real-time polymerase chain reaction (qPCR), we detected the presence of human herpesvirus (HHV)-6A and B genomic sequences in DNA isolated from peripheral blood mononuclear cells (PBMCs) and buccal swabs. HHV-6-specific IgG and IgM class antibodies, along with proinflammatory cytokine levels, were measured using enzyme-linked immunosorbent assay (ELISA) and bead-based multiplex assays. Additionally, the gut microbiome was analyzed through next-generation sequencing. HHV-6B was more frequently detected in the PBMCs of FM patients. FM patients with a body mass index (BMI) of 30 or higher exhibited elevated cytokine levels compared to the control group with the same BMI range. Gut microbiome analysis revealed significant differences in both α-diversity and β-diversity between the FM and control groups, indicating a shift in species abundance in the FM group. Full article

Brain tumors are among the most lethal diseases, and early detection is crucial for improving patient outcomes. Currently, magnetic resonance imaging (MRI) is the most effective method for early brain tumor detection due to its superior imaging quality for soft tissues. However, manual analysis of brain MRI scans is prone to errors, largely influenced by the radiologists’ experience and fatigue. To address these challenges, computer-aided diagnosis (CAD) systems are more significant. These advanced computer vision techniques such as deep learning provide accurate predictions based on medical images, enhancing diagnostic precision and reliability. This paper presents a novel CAD framework for multi-class brain tumor classification. The framework employs six pre-trained deep learning models as the base and incorporates comprehensive data preprocessing and augmentation strategies to enhance computational efficiency. To address issues related to transparency and interpretability in deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize the decision-making processes involved in tumor classification from MRI scans. Additionally, a user-friendly Brain Tumor Detection System has been developed using Streamlit, demonstrating its practical applicability in real-world settings and providing a valuable tool for clinicians. All simulation results are derived from a public benchmark dataset, showing that the proposed framework achieves state-of-the-art performance, with accuracy approaching 99% in ResNet-50, Xception, and InceptionV3 models. Full article

The relationship between daytime sleepiness and heart rate asymmetry (HRA) during the transition from evening to night is crucial for understanding autonomic regulation and its implications for alertness. This study aims to investigate how daytime sleepiness influences HRA dynamics from evening to night and how situational sleepiness correlates with HRA metrics. HRA metrics were calculated at 8 P.M., 9 P.M., and 10 P.M. in 50 participants, categorized into ‘Lower Normal’ and ‘Higher Normal’ daytime sleepiness groups based on Epworth Sleepiness Scale (ESS) scores. Situational sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) and Stanford Sleepiness Scale (SSS). The results demonstrated that individuals with ‘Higher Normal’ daytime sleepiness exhibited lower HRA metrics at 10 P.M. compared to those with ‘Lower Normal’ daytime sleepiness, supporting the suggestion that higher daytime sleepiness correlates with reduced parasympathetic activity and diminished autonomic responsiveness. Significant negative correlations between situational sleepiness and HRA metrics were observed in the ‘Higher Normal’ group, particularly with the SSS. Therefore, increased daytime sleepiness affects HRA dynamics by decreasing parasympathetic activity and altering autonomic regulation at the beginning of the biological night (10 P.M.). These findings suggest potential applications for enhancing drowsiness detection and managing fatigue in safety-critical environments. Full article