Fatigue Monitoring System Research Articles

Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms

Due to its major safety applications, including safe driving, mental fatigue estimation is a rapidly growing research topic in the engineering field. Most current mental fatigue monitoring systems analyze brain activity through electro-encephalography (EEG). Yet eye blink analysis can also be added to help characterize fatigue states. It usually requires the use of additional devices, such as EOG electrodes, uncomfortable to wear, or more expensive eye trackers. However, in this article, a method is proposed to evaluate eye blink parameters using frontal EEG electrodes only. EEG signals, which are generally corrupted by ocular artifacts, are decomposed into sources by means of a source separation algorithm. Sources are then automatically classified into ocular or non-ocular sources using temporal, spatial and frequency features. The selected ocular source is back propagated in the signal space and used to localize blinks by means of an adaptive threshold, and then to characterize detected blinks. The method, validated on 11 different subjects, does not require any prior tuning when applied to a new subject, which makes it subject-independent. The vertical EOG signal was recorded during an experiment lasting 90min in which the participants’ mental fatigue increased. The blinks extracted from this signal were compared to those extracted using frontal EEG electrodes. Very good performances were obtained with a true detection rate of 89% and a false alarm rate of 3%. The correlation between the blink parameters extracted from both recording modalities was 0.81 in average.

A real-time fatigue monitoring and analysis system for lower extremity muscles with cycling movement.

A real-time muscle fatigue monitoring system was developed to quantitatively detect the muscle fatigue of subjects during cycling movement, where a fatigue progression measure (FPM) was built-in. During the cycling movement, the electromyogram (EMG) signals of the vastus lateralis and gastrocnemius muscles in one leg as well as cycling speed are synchronously measured in a real-time fashion. In addition, the heart rate (HR) and the Borg rating of perceived exertion scale value are recorded per minute. Using the EMG signals, the electrical activity and median frequency (MF) are calculated per cycle. Moreover, the updated FPM, based on the percentage of reduced MF counts during cycling movement, is calculated to measure the onset time and the progressive process of muscle fatigue. To demonstrate the performance of our system, five young healthy subjects were recruited. Each subject was asked to maintain a fixed speed of 60 RPM, as best he/she could, under a constant load during the pedaling. When the speed reached 20 RPM or the HR reached the maximal training HR, the experiment was then terminated immediately. The experimental results show that the proposed system may provide an on-line fatigue monitoring and analysis for the lower extremity muscles during cycling movement.

Strain response estimation for the fatigue monitoring of an offshore truss structure

Fatigue damage accumulation is an outstanding issue in ocean engineering due to long-term cyclic mechanical behaviours resulting from wind, waves and currents. Fatigue monitoring of offshore truss structures has been limited by the costs and techniques of current strain sensors. Aiming to estimate the unmeasured response of structural members, for which there are no available sensors, this paper proposes a strain response estimation strategy using three types of state-space formulations and a Kalman Filtering (KF) process. A strain modal coordinate based state-space model was developed, especially for large engineering structures. The theoretical approaches were evaluated using the numerical simulations based on deterministic and stochastic excitations. The algorithm can be integrated with the existing fatigue monitoring system as an early warning tool.

Effects of fatigue on the physiological parameters of labor employees

Human factors have contributed to the causes of several accidents. The physiological parameters change law of labor employees before and after fatigue is researched in this paper. The purpose of this research is to find the relationship between physiological parameters and fatigue, design corresponding fatigue monitoring and early warning system, reduce the accident rate and optimize the working environment. The labor employees’ heart rate, RR interval, respiration rate and blood pressure under fatigue state are determined in the laboratory. The change law of labor employees’ heart rate, RR interval, respiratory rate and blood pressure are analyzed based on the paired samples t test by using SPSS software. The results show that labor employees’ heart rate and RR interval have no significant difference before and after fatigue. Labor employees’ respiration rate has significant difference before and after fatigue: the respiration rate decreased after fatigue. Labor employees’ blood pressure has significant difference before and after fatigue: blood pressure of labor employees after fatigue can be elevated. The results provide a richer theoretical and practical basis in what criteria are used to determine the fatigue. It has important significance in the safety management field and accident prevention.

Driver Fatigue Monitoring Based on Head and Facial Features Using Hierarchical Bayesian Method

Because fatigue monitoring based on the image of the non-contact measurement is single and low accuracy, a novelty driver fatigue monitoring system based on multivariate hierarchical Bayesian network is proposed. The system mainly includes four modules following: face region detecting, eyelid closure judging, head region positioning, and fatigue analyzing. The eye region is positioned precisely by the method of gray projection function, the binary image which contains the whole eye information using self-adapting threshold method is obtained, and then driver fatigue monitoring system based on hierarchical Bayesian network is used to evaluate the fatigue level of the driver. The experimental results show that the fatigue monitoring accuracy is up to 90% in specific conditions, it’s effective to improve the detection accuracy compared to the other method.

A Wireless Fatigue Monitoring System Utilizing a Bio-Inspired Tree Ring Data Tracking Technique

Fatigue, a hot scientific research topic for centuries, can trigger sudden failure of critical structures such as aircraft and railway systems, resulting in enormous casualties as well as economic losses. The fatigue life of certain structures is intrinsically random and few monitoring techniques are capable of tracking the full life-cycle fatigue damage. In this paper, a novel in-situ wireless real-time fatigue monitoring system using a bio-inspired tree ring data tracking technique is proposed. The general framework, methodology, and verification of this intelligent system are discussed in details. The rain-flow counting (RFC) method is adopted as the core algorithm which quantifies fatigue damages, and Digital Signal Processing (DSP) is introduced as the core module for data collection and analysis. Laboratory test results based on strain gauges and polyvinylidene fluoride (PVDF) sensors have shown that the developed intelligent system can provide a reliable quick feedback and early warning of fatigue failure. With the merits of low cost, high accuracy and great reliability, the developed wireless fatigue sensing system can be further applied to mechanical engineering, civil infrastructures, transportation systems, aerospace engineering, etc.

DEVELOPMENT OF GREEN’S FUNCTION APPROACH CONSIDERING TEMPERATURE-DEPENDENT MATERIAL PROPERTIES AND ITS APPLICATION

About 40% of reactors in the world are being operated beyond design life or are approaching the end of their life cycle. During long-term operation, various degradation mechanisms occur. Fatigue caused by alternating operational stresses in terms of temperature or pressure change is an important damage mechanism in continued operation of nuclear power plants. To monitor the fatigue damage of components, Fatigue Monitoring System (FMS) has been installed. Most FMSs have used Green's Function Approach (GFA) to calculate the thermal stresses rapidly. However, if temperature-dependent material properties are used in a detailed FEM, there is a maximum peak stress discrepancy between a conventional GFA and a detailed FEM because constant material properties are used in a conventional method. Therefore, if a conventional method is used in the fatigue evaluation, thermal stresses for various operating cycles may be calculated incorrectly and it may lead to an unreliable estimation. So, in this paper, the modified GFA which can consider temperature-dependent material properties is proposed by using an artificial neural network and weight factor. To verify the proposed method, thermal stresses by the new method are compared with those by FEM. Finally, pros and cons of the new method as well as technical findings from the assessment are discussed.

Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel

Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 Hz from the driver's palms as they stay on a pair of conductive fabric electrodes located on the steering wheel. Practical tests were conducted using an embedded ECG sensor with a wireless sensor node, and their performance was assessed under non-stop 2 h driving test. The ECG signals were measured and transmitted wirelessly to a base station connected to a server PC in personal area network environment. The driver's health condition such as the normal, fatigued and drowsy states was analysed by evaluating the heart rate variability in the time and frequency domains.

Classification of driver fatigue expressions by combined curvelet features and gabor features, and random subspace ensembles of support vector machines

In order to develop Human-centric Driver Fatigue Monitoring Systems HDFMS with aims to increase driving safety, an efficient combined features extraction from Curvelet transform and Gabor wavelet transform for fatigue expressions descriptions of vehicle drivers is proposed, and Random Subspace Ensemble RSE of Support Vector Machines SVMs with polynomial kernel as the base classifier is then exploited for classification of three predefined fatigue expressions classes, namely, awake expressions, moderate fatigue expressions, and severe fatigue expressions. The results of holdout and cross-validation experiments show that CF by RSE of SVMs with polynomial kernel outperforms other seven classifiers, i.e., Curvelet features by SVMs classifier, Gabor features by SVMs classifier, CF by five individual SVMs classifiers. With CF and RSE of SVMs with polynomial kernel, the classification accuracies of drivers' fatigue expressions are over 90% in both of the holdout and cross-validation experiments, which show the proposed approach of combined features extraction and RSE of SVMs can be used for developing Human-centric Driver Fatigue Monitoring Systems to increase driving safety.

Classification of driver fatigue expressions by combined curvelet features and gabor features, and random subspace ensembles of support vector machines

In order to develop Human-centric Driver Fatigue Monitoring Systems HDFMS with aims to increase driving safety, an efficient combined features extraction from Curvelet transform and Gabor wavelet t...

Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

A driver fatigue monitoring and detection system with high accuracy could be a valuable countermeasure to decrease fatigue-related traffic accidents. This study proposes methods for drowsiness detection based on electroencephalogram (EEG) power spectrum analysis. First, a new algorithm is proposed for independent component analysis with reference (ICA-R) for electrooculography artefacts removal. Comparison is then carried out between the proposed ICA-R algorithm and an adaptive filter. Secondly, 75 EEG spectrum features are extracted from the cleaned EEG. Among all the EEG spectrum-related features, 40 key features are selected by support vector machine recursive feature elimination to improve the performance of the classifier. The validation results show that 86% of the driver's drowsiness states can be accurately detected among drivers, who participate a driving simulator study.

Driver's fatigue expressions recognition by combined features from pyramid histogram of oriented gradient and contourlet transform with random subspace ensembles

Human-centric driver fatigue monitoring systems (DFMS) with integrated sensing, processing and networking aim to find solutions for traffic accidents and other relevant issues. A novel, efficient combined features extraction approach from Pyramid Histogram of Oriented Gradients (PHOG) and contourlet transform (CT) for fatigue expression descriptions of vehicle drivers is proposed, and a random subspace ensemble (RSE) of linear perception (LP) classifiers as the base classifier is then exploited for the classification of three predefined fatigue expressions classes, namely, awake expressions, moderate fatigue expressions and severe fatigue expressions. Holdout and cross-validation experiments are created, and the results show that combined features by RSE of LP classifiers outperform the other seven classifiers, that is, PHOG features by LP classifier, CT features by LP classifier and combined features by five individual LP classifiers. With combined features and RSE of LP classifiers, the average classification accuracies of three fatigue expression classes are over 92% in both the holdout and cross-validation experiments. Among the three fatigue expression classes, the class of severe fatigue expressions is the most difficult to recognise, and the classification accuracy is over 84% in both the holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction method and RSE of LP classifiers in developing DFMS.

A wireless body measurement system to study fatigue in multiple sclerosis

Fatigue is reported as the most common symptom by patients with multiple sclerosis (MS). The physiological and functional parameters related to fatigue in MS patients are currently not well established. A new wearable wireless body measurement system, named Fatigue Monitoring System (FAMOS), was developed to study fatigue in MS. It can continuously measure electrocardiogram, body-skin temperature, electromyogram and motions of feet. The goal of this study is to test the ability of distinguishing fatigued MS patients from healthy subjects by the use of FAMOS. This paper presents the realization of the measurement system including the design of both hardware and dedicated signal processing algorithms. Twenty-six participants including 17 MS patients with fatigue and 9 sex- and age-matched healthy controls were included in the study for continuous 24 h monitoring. The preliminary results show significant differences between fatigued MS patients and healthy controls. In conclusion, the FAMOS enables continuous data acquisition and estimation of multiple physiological and functional parameters. It provides a new, flexible and objective approach to study fatigue in MS, which can distinguish between fatigued MS patients and healthy controls. The usability and reliability of the FAMOS should however be further improved and validated through larger clinical trials.

Assessment of Remaining Useful Life of Power Plant Steam Generators – a Standardized Industrial Application

The Web based condition monitoring and diagnostic system “Boiler Fatigue Monitoring” enables on-line assessment of cumulative boiler creep and low cycle fatigue according to the European standard EN 12952-3/4 issued in 2001.The application is employed as autonomous module as well as fully integrated into the Siemens process control system SPPA-T3000 and is becoming more and more a standard part of the power plant instrumentation and control (I&C).The Fatigue Monitoring System (FMS) is a standard industrial application for both, new built power plants and retrofits of existing units of any kind. The system is not limited to Siemens I&C systems, it is possible to integrate FMS also into power plants with I&C systems of other suppliers. FMS is also capable for calculating the remaining lifetime for boilers designed according to the American standard ASME VIII-2.

Fatigued patients with multiple sclerosis can be discriminated from healthy controls by the recordings of a newly developed measurement system (FAMOS): a pilot study

Purpose: To explore the difference between fatigued patients with multiple sclerosis (MS) and matched healthy controls by applying a newly developed physiological and functional data recording system. Methods: A portable wireless measurement system, named FAtigue MOnitoring System (FAMOS), has been developed, which can continuously measure electrocardiogram (ECG), body skin temperature, electromyogram, and motion parameters. In a pilot study, 17 fatigued MS patients (fatigue severity scale (FSS) = 53.9 SD 5.4) and 9 healthy matched controls (FSS = 27.2 SD 6.8) were recruited and monitored by FAMOS during the execution of functional (36 m walk test, 5- and 50-repetition sit-to-stand test) and cognitive (short-term memory tests) tests. Furthermore, oxygen saturation (SPO2) was measured during the functional and cognitive tests and during rest periods between these tests. Results: Recordings from FAMOS indicate that fatigued MS patients have reduced standard deviation (SD) of the heart rate (HR) during the short-term memory test, reduced high frequency (HF) component power spectrum (representing parasympathetic activation) at rest after walk test, and higher ratio of low frequency (LF) to HF (LF/HF) during 50-repetition sit-to-stand test. Conclusions: FAMOS recordings can discriminate fatigued MS patients from healthy controls. The data indicate that fatigued MS patients have vagus nerve dysfunction during cognitive tests and disturbed sympathovagal balance during stressful physical tests.Implications for RehabilitationFatigue is reported as the most common symptom by patients with multiple sclerosis, but the mechanism of this symptom is still not clearly understood.Wireless body sensor networks is one of the most promising concepts for healthcare and rehabilitation providing long-term health monitoring of subjects under conditions without constraining the subjects’ normal daily activities, but such system has not been used in the study of fatigue in MS.The main findings of this pilot study were that FAMOS can discriminate fatigued MS patients from healthy controls, and recordings extracted from specific monitoring periods indicate autonomic dysfunction in MS patients characterized by primary fatigue.

Verification of an individual aircraft fatigue monitoring system

Fatigue monitoring of airframes has developed over the decades to the stage where it is now incumbent for the certification of fighter type aircraft to incorporate a fatigue monitoring system. These systems typically collect operational data for the calculation of the airframe’s safe-life or crack inspection intervals. Many of these systems are complex, incorporating such features as data integrity checking, strain gauge calibration algorithms and damage calculation algorithms to name a few. Whilst it may be possible to validate the robustness and accuracy of specific system components (e.g. the damage algorithm can be tested against fatigue coupon results), the verification of the performance of the in-service system as a whole presents a much bigger challenge.In this paper, the verification of the Royal Australian Air Force’s F/A-18A/B Hornet individual aircraft fatigue monitoring system is outlined. The availability of a significant number of ex-service centre fuselage sections with known usage has facilitated this effort. Using an enhanced teardown procedure, in-service fatigue crack growth has been identified at a significant number of locations. All the in-service cracking corresponded to the same locations found cracked in the fatigue certification full-scale test article that was used to calibrate the usage monitoring system, so that by comparing the measured in-service growth with the test-demonstrated growth the functionality of the monitoring system could be assessed. This assessment should reveal the effectiveness of the system in providing robust fatigue life expended indices to help ensure that structural integrity boundaries are not exceeded. For this comparison, the crack growth was measured using quantitative fractography.It is believed that this work is the first example of using the crack growth in retired structure of known usage to verify a fatigue tracking system that incorporated significant aircraft structural integrity elements including tracking philosophy, structural fatigue lifing methodology, full-scale fatigue test results, design standard interpretation and retirement considerations.

Structural resource of the aircraft IAR-99 SOIM

Aircraft structure fatigue monitoring has been developed over decades presently reaching the stage where it became mandatory for all combat aircraft to be equipped with an airborne fatigue monitoring system. These systems usually collect operational data for calculating the safe fatigue life or inspection interval for the aircraft structure. This paper presents an analysis of the current state of fatigue monitoring systems on the IAR-99 SOIM based on the experience of international fatigue monitoring programs and analysis of structure resource improvement. Aspects related to strain gauge and calibration, flight parameter data gathering, data integrity, comparison with fatigue test results and fatigue damage models are also investigated by means of flight tests.

Research on Driving Fatigue Monitoring Based on Tree-Structure Classifier

The driving fatigue monitoring system based on tree structure classifier is presented. The realtime fatigue monitoring system is established by training face and eye tree-structure classifiers which consists of many strong classifiers. Each strong classifier choose several weak classifiers by AdoBoost recursive method and each weak classifier is characterized by single Haar-like features. The whole Tree-structure is constructed by clustering branches recursive algorithm. Experiment has shown that the driving fatigue monitoring system based on tree-structure classifiers is very reliable. Introduction

Realtime Driving Fatigue Monitoring System with Optical Function Materials Based on Controllable Infrared Light

A feasible driver fatigue monitoring system based on controllable infrared light is presented. This system adopts controllable infrared light to get the light and dark image of the pupil, and preprocess the image using differences, gaussian smoothing filter and binaryzation. Then the location of driver’s eyes is detected by template matching algorithm. Finally, Kalman Filter is implemented to track the eyes in order to detect the state of the eyes. This mechanism features accuracy, responsiveness and adaptability to various situations.

A Methodology for Online Fatigue Monitoring With Consideration of Temperature-Dependent Material Properties Using Artificial Parameter Method

Abstract Following the basis of the ASME codes, the major nuclear components are designed to successfully avoid the fatigue failure. However, such design is generally very conservative and it is necessary to accurately assess the fatigue life of the components for the optimal life. The assessment of fatigue damage accumulation due to the thermal transients is currently performed via online fatigue monitoring systems. The algorithms for online calculation of thermal stress are one of the main components of these systems and are often based on the Green function technique (GFT), in which machine parameters such as fluid temperatures, pressures, and flow rates are converted into metal temperature transients and thermal stresses. However, since the GFT is based upon the linear superposition principle, it cannot be directly used when the temperature-dependent material properties are considered. This paper presents a methodology to consider the temperature- dependent material properties using artificial parameter method. Two cases are presented to compare the results calculated from the proposed models with those calculated by finite element method (FEM). It is found that the temperature-dependent material properties have significant influence on the maximum peak stresses which can be accurately captured by the models proposed in this work.