Fatigue Monitoring System Research Articles

Wearable network for multilevel physical fatigue prediction in manufacturing workers.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

Project Awakesure: Intelligent Drowsiness Detection Using Eye Tracking

Being sleepy or drowsy is referred to as being drowsy. A person who is sleepy may feel exhausted or lethargic and struggle to stay awake. People who are sleepy tend to be less attentive and may even nod off, though they can still be awakened. An increasing number of vocations nowadays call for sustained focus. In order for drivers to respond quickly to unexpected incidents, they must maintain a watchful eye on the road. Many road incidents are directly caused by tired drivers. In order to drastically lower the frequency of fatigue-related auto accidents, it is crucial to develop technologies that can identify and alert a driver to a poor psychophysical state. However, there are many challenges in developing systems that can quickly and accurately recognize a driver's signs of fatigue. Using vision-based technology is one technological option for implementing driver fatigue monitoring systems. The available driver drowsiness detection systems are described in this article. Here, we are assessing the driver's level of sleepiness utilizing his visual system. The automated system for preventing accidents and monitoring sleepy drivers developed for this study is based on detecting variations in the length of eye blinks. Our recommended technique makes use of the eyes' postulated horizontal symmetry property to identify visual changes in eye positions. Our novel approach precisely positions a standard webcam in front of the driver's seat to identify eye blinks. It will identify the eyeballs based on a specific EAR (Eye Aspect Ratio).

Driver body condition monitoring system based on human-computer interaction

Tired driving is still a significant cause of traffic accidents, often causing huge economic losses to society.Therefore, how to avoid driver fatigue has become a meaningful problem. The purpose of this paper is to review and summarize various research methods of driver fatigue monitoring system at home and abroad, so as to reduce the probability of traffic accidents caused by fatigue driving. The main content of this paper is to review the existing fatigue driving monitoring technologies such as electrocardiogram (ECG), electroencephalogram (EEG), HRV, eye tracking system, CNN, facial state analysis, emotion recognition, 5G and brain wave recognition, and infer the fatigue level of drivers by using these technologies to extract various clues that usually represent the level of human vigilance. Assess cognitive load and identify emotional states to monitor tired driving. By discussing the advantages and disadvantages of various fatigue monitoring systems at home and abroad, this paper proposes an innovative method, which transforms the previous single-mode monitoring system into a multi-mode monitoring system, integrates multiple biosensors, extracts relevant features from the biosensor data and processes the data reasonably in order to understand the drivers physiological state more comprehensively. This method is expected to improve the accuracy of fatigue monitoring system.

Workers Fatigue Monitoring for Well-being Improvement in Manufacturing

In Industry 5.0, worker well-being is paramount for organizational resilience and sustainability. Physical fatigue, work-life balance, and job competency significantly impact worker welfare and, therefore, efficiency and effectiveness. This study collects data in different industrial scenarios using non-invasive wearable devices for dynamic data and questionnaires for quasi-static data. Using Machine Learning algorithms, including Random Forest and Feedforward Neural Network models, the study predicts the physical fatigue of workers across multi-class and binary classifications. The developed Fatigue Monitoring System software integrates these models to monitor fatigue and improve worker well-being.

The effect of prolonged walking on leg muscle activity patterns and vulnerability to perturbations

Understanding the consequences and ecological relevance of muscle fatigue is important to guide the development of strategies to preserve independence. However, few studies have examined walking-related fatigue and the effects on walking instability. Our purpose was to investigate the effects of prolonged walking on leg muscle activity and vulnerability to balance perturbations. Eighteen healthy young adults completed a 30-min walking trial at their preferred walking speed while leg muscle activities were recorded. Before and after the 30-min walk, participants responded to five 5% body weight lateral force perturbations. Time-frequency analysis with wavelet transformation and principal component analyses assessed neuromuscular adaptations of muscles to prolonged walking. Following prolonged walking, we observed a time-dependent increase in EMG intensities at slower frequencies for the soleus and tibialis anterior and a decrease in mean amplitudes for the soleus, lateral gastrocnemius, and semitendinosus. Mean mediolateral CoM displacement following perturbations averaged 21% larger after the 30-min walk. Our results suggest that walking for 30 min at a comfortable speed elicits complex neuromuscular adaptations indicative of local muscle fatigue and an increased vulnerability to walking balance perturbations. These findings could inform fatigue monitoring systems or walking assistive devices aimed at reducing walking-related fatigue and maintaining independent mobility.

Perancangan Sistem Pemantauan Kelelahan Driver Berbasis IoT (Internet Of Things) Yang Adaptif Untuk Transportasi Makanan Segar

In the logistics industry, fresh food transportation requires effective management of driver fatigue to maintain safety and service quality. With the increasing digital connectivity, Internet of Things (IoT) technology has become a promising solution to monitor driver fatigue in real-time. This research aims to design an adaptive IoT-based driver fatigue monitoring system specifically for fresh food transportation. Through a case study in the logistics industry, we identified specific challenges and needs in driver fatigue management. Based on the identification results, we developed a system that utilizes IoT sensors integrated in vehicles and drivers' smartphones. The system automatically monitors the driver's physical condition and behavior and provides real-time alerts if signs of fatigue are detected. With an adaptive approach, the system can customize recommendations and interventions according to the measured fatigue level. The results of the case study show that the designed system is able to reduce the risk of driver fatigue, increase transportation safety, and improve service quality in fresh food transportation. This research contributes to the practical understanding of the implementation of IoT technology in driver fatigue management, particularly in the context of the logistics and fresh food transportation industries.

Integrated Thermofluid Lumped Parameter Model for Analyzing Hemodynamics in Human Fatigue State

It is well known that driving while fatigued is dangerous and can lead to serious traffic accidents. However, there is a lack of studies on the mechanism of fatigue. This paper sought to infer changes in the cardiovascular system through hand and head skin temperature peripheral factors via an integrated lumped parameter model. A multi-layer inner structure with variable blood perfusion was used to construct a full-body thermal model. The cardiovascular system model provided blood perfusion using lumped parameters. The peripheral resistance and heart rate in the cardiovascular system model were adjusted to match the experimental temperatures of the head and hands obtained from induced fatigue experiments. The simulation results showed that the heart rate and blood pressure decreased, and the peripheral skin resistance of the hands and head increased after fatigue. A decrease in heart rate and an increase in peripheral resistance affect the magnitude of blood flow to the periphery of the body, leading to a decrease in skin temperature during fatigue. The present integrated model elucidates a key effect of human fatigue on the cardiovascular system, which is expected to help improve the accuracy of fatigue monitoring systems.

Sport Fatigue Monitoring and Analyzing Through Multi-Source Sensors

During the process of daily training or competition, athletes may suffer the situation that the load exceeds the body's bearing capacity, which makes the body's physiological function temporarily decline. It is one of the characteristics of sports fatigue. Continuous sports fatigue may incur permanent damage to the athletes if they cannot timely get enough rest to recover. In order to solve this issue and improve the quality of athlete's daily training, this paper establish a fatigue monitoring system by using multi-source sensors. First, the sEMG signals of athlete are collected by multi-source sensors which are installed in a wearable device. Second, the collected sEMG signals are segmented by using fixed window to be converted as Mel-frequency cepstral coefficients (MFCCs). Third, the MFCC features are used learn a Gaussian processing model which is used to monitor future muscle fatigue status. The experiments show that the proposed system can recognize more than 90% muscle fatigue states.

A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition

Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed better performance than the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data.

Predicting Operators’ Fatigue in a Human in the Artificial Intelligence Loop for Defect Detection in Manufacturing

Quality inspection, typically performed manually by workers in the past, is now rapidly switching to automated solutions, using artificial intelligence (AI)-driven methods. This elevates the job function of the quality inspection team from the physical inspection tasks to tasks related to managing workflows in synergy with AI agents, for example, interpreting inspection outcomes or labeling inspection image data for the AI models. In this context, we have studied how defect inspection can be enhanced, providing defect hints to the operator to ease defect identification. Furthermore, we developed machine learning models to recognize and predict operators’ fatigue. By doing so, we can proactively take mitigation actions to enhance the workers’ well-being and ensure the highest defect inspection quality standards. We consider such processes to empower human and non-human actors in manufacturing and the sociotechnical production system. The paper first outlines the conceptual approach for integrating the operator in the AI-driven quality inspection process while implementing a fatigue monitoring system to enhance work conditions. Furthermore, it describes how this was implemented by leveraging data and experiments performed for a real-world manufacturing use case.

EEG-based mental fatigue detection using linear prediction cepstral coefficients and Riemann spatial covariance matrix

Objective. Establishing a mental fatigue monitoring system is of great importance as for severe fatigue may cause unimaginable consequences. Electroencephalogram (EEG) is often utilized for mental fatigue detection because of its high temporal resolution and ease of use. However, many EEG-based approaches for detecting mental fatigue only take into account the feature extraction of a single domain and do not fully exploit the information that EEG may offer. Approach. In our work, we propose a new algorithm for mental fatigue detection based on multi-domain feature extraction and fusion. EEG components representing fatigue are closely related in the past and present because fatigue is a dynamic and gradual process. Accordingly, the idea of linear prediction is used to fit the current value with a set of sample values in the past to calculate the linear prediction cepstral coefficients (LPCCs) as the time domain feature. Moreover, in order to better capture fatigue-related spatial domain information, the spatial covariance matrix of the original EEG signal is projected into the Riemannian tangent space using the Riemannian geometric method. Then multi-domain features are fused to obtain comprehensive spatio-temporal information. Main results. Experimental results prove the suggested algorithm outperforms existing state-of-the-art methods, achieving an average accuracy of 87.10% classification on the public dataset SEED-VIG (three categories) and 97.40% classification accuracy (two categories) on the dataset made by self-designed experiments. Significance. These findings show that our proposed strategy perform more effectively for mental fatigue detection based on EEG.

A Systematic Survey of Driving Fatigue Monitoring

The appearance of fatigue is not conducive to driving activities because this state can affect driving performance and even cause life-threatening consequences. To reduce various traffic accidents caused by fatigue, researchers began to explore effective fatigue monitoring systems to detect this unfavorable state early. This paper systematically surveys the research on driving fatigue monitoring from three aspects: data acquisition, feature extraction, and fatigue assessment. Furthermore, this paper analyzes the differences between active and passive fatigue, fatigue and sleepiness, as well as fatigue and transportation scenarios. Finally, some open issues on driving fatigue monitoring are proposed, which will be the key research directions for future developments.

Environmental and Work Factors That Drive Fatigue of Individual Haul Truck Drivers

Many factors influence the fatigue state of human beings, and fatigue has a significant adverse effect on the health and safety of the haulage operators in the mine. Among various fatigue monitoring systems in mine operations, currently, the Percentage of Eye Closure (PERCLOS) is common. However, work and other environmental factors influence the fatigue state of haul truck drivers; PERCLOS systems do not consider these factors in their modeling of fatigue. Therefore, modeling work and environmental factors’ impact on individual operations fatigue state could yield interesting insights into managing fatigue. This study provides an approach of using operational data sets to find the leading indicators of the operators’ fatigue. A machine learning algorithm is used to model the fatigue of the individual. eXtreme Gradient Boosting (XGBoost) algorithm is chosen for this model because of its efficiency, accuracy, and feasibility, which integrates multiple tree models and has stronger interpretability. A significant number of negative and positive samples are created from the available data to increase the number of datasets. Then, the results are compared with other existing models. A selected algorithm, along with a big data set was able to create a comprehensive model. The model was able to find the importance of the individual factors along with work and environmental factors among operational data sets.

Fatigue Assessment of Wind Turbine Towers: Review of Processing Strategies with Illustrative Case Study

Wind turbines are structures predominantly subjected to dynamic loads throughout their period of life. In that sense, fatigue design plays a central role. Particularly, support structure design might be conservative with respect to fatigue, which may lead to a real fatigue life of considerably more than 20 years. For these reasons, the implementation of a fatigue monitoring system can be an important advantage for the management of wind farms, providing the following outputs: (i) estimation of the evolution of real fatigue condition; (ii) since the real condition of fatigue damage is known, these results could be an essential element for a decision about extending the lifespan of the structure and the possibility of repowering or overpowering; and (iii) the results of the instrumented wind turbines can be extrapolated to other wind turbines of the same wind farm. This paper reviews the procedures for calculating the fatigue damage of wind turbine towers using strain measurements. The applicability of the described procedures is demonstrated with experimental data acquired in an extensive experimental campaign developed at Tocha Wind Farm, an onshore wind farm located in Portugal, exploring the impact of several user-defined parameters on the fatigue results. The paper also includes the description of the data processing needed to convert raw measurements into bending moments and several validation and calibration steps.

0140 Operationalizing Sleep for the Military: Advancing an Infrastructure for Fatigue Risk Management with Wearable Technology

Abstract Introduction A recent investigation on fatigue within United States (US) Naval Surface Force by the Government Accountability Office recommended that the US Navy incorporate methods to empirically monitor and manage fatigue-related threats. In response, a feasibility effort was undertaken to assess two commercial wearable devices previously evaluated against polysomnography by our group for potential use within a novel fatigue monitoring system onboard US warships. Methods 501 Sailors from three warships (415 men, 85 women, 1 response missing; 29.1±7.4 years, mean±SD) were included in analyses. During multi-day missions at sea, participants were instructed to continuously wear a wrist-worn Fatigue Science Readiband and an Oura Ring. Participants met with the research team daily to sync wearable devices and complete self-ratings of sleep and fatigue on a tablet. While returning to port, participants completed a user experience questionnaire that included 5-point scales for device comfort and interference with daily activities (larger values indicate greater comfort or interference). Results There were no overall differences in total sleep time (TST) and sleep efficiency (SE) between devices (TST: Ring=352.23±87.1, Readiband=353.89±97.79; p=0.45, d=-0.01; SE: Ring=84.59±7.08, Readiband=84.20±10.24; p=0.06, d=0.04). There were 9 days for TST and 4 days for SE (of 66 total days) that resulted in effect sizes >0.50 between devices (for >0.20: TST, 39/66; SE, 29/66). Preliminary analyses between TST and self-reported exhaustion and inability to function suggest predictive relationships with the wearables (Ring: r=-0.20; Readiband: r=-0.21), with no relationship with SE across devices (both r<0.01). Average comfort ratings for Ring (3.74) exceeded Readiband (3.59), but this difference was small (p=0.04, d=0.12). Interference with daily activities was also similar between devices (Ring=2.10, Readiband=2.20; p=0.14, d=0.08). Conclusion Both commercial wearables performed similarly for their TST and SE assessments, and TST was associated with self-rated fatigue within this operational environment. While the ring form factor was selected more for comfort than the wrist form factor, the difference was small. Collectively, findings indicate that both devices show promise for operational use within fatigue monitoring systems. Further testing is being conducted to better understand the strengths and limitations of wearable devices for monitoring sleep in warship environments. Support (If Any) Military Operational Medicine Research Program

A Novel Physical Fatigue Assessment Method Utilizing Heart Rate Variability and Pulse Arrival Time towards Personalized Feedback with Wearable Sensors.

This paper proposes a novel method for physical fatigue assessment that can be applied in wearable systems, by utilizing a set of real-time measurable cardiovascular parameters. Daylength measurements, including a morning test set, physical exercise during the day, and an afternoon test set were conducted on 16 healthy subjects (8 female and 8 male). To analyze cardiovascular parameters for physical fatigue assessment, electrocardiography, pulse wave and blood pressure were measured during the test sets. The fatigue assessment questionnaire score, reaction time, countermovement jump height and hand grip strength were also measured and used as reference parameters. This study demonstrates that (i) the compiled test battery can selectively assess the rested vs. physically-fatigued states; (ii) the obtained linear support-vector machine, trained using the heart rate variability based parameter (F-score 0.842, accuracy 0.813) and pulse arrival time based parameter (F-score 0.875, accuracy 0.875) shows a promising ability to classify between the physically mildly fatigued and significantly fatigued states. Despite the somewhat limited study group size, the results of the study are unique and provide a significant advancement on the existing physical fatigue assessment methods towards a personalized and continuous real-time fatigue monitoring system with wearable sensors.

A Method for Monitoring Vibrational Fatigue of Structure and Components

Abstract A vibrational fatigue monitoring system has been developed by Framatome to assess, in real-time, the evolution of industrial structures, systems and components lifetime expectancy. Its originality comes from the fact that only one or a few acceleration measurements are necessary to reconstruct the complete stress history in the whole structure, including on welds or bolted connections that could not have been directly instrumented. From this stress history, a fatigue analysis with a rainflow counting algorithm is conducted and the cumulative usage factor of each weld or bolt is determined. The remaining life duration is then estimated. The method has been numerically and experimentally validated in that sense that the reconstructed stress histories were successfully compared to direct stress calculations and measurements. The system was then installed on five industrial structures submitted to transient dynamic excitations. It is expected that it will soon find further applications notably in monitoring vibrations induced during power plants transients that may induce some temporary resonance of piping equipment. Finally, the vibrational monitoring system can also be combined with a thermal fatigue monitoring system, many of which are already deployed, at least on nuclear power plants, and the reconstructed stresses might include both thermal and mechanical effects. Installing such a fatigue monitoring on a set of sensitive systems and components could be a valuable brick in the present trend of building digital twins of power plants or other industrial structures.

Perception-Free Calibration of Eye Opening and Closing Threshold for Driver Fatigue Monitoring

Analyzing the opening and closing states of eyes and mouths by detecting the driver’s face feature points is an effective method for judging driver fatigue. However, in practical engineering applications, with the expansion of user groups, the false identification problem caused by the differentiation of individual facial features of drivers is prominent, especially for people with small eyes. To solve this problem, this paper uses the Mediapipe Facemesh module to detect face feature points and designs a perception-free calibration method for setting personalized eye opening and closing threshold combined with head postures. Compared with the traditional method of setting a fixed threshold, the precision of eye state recognition is improved by 36.4%. Finally, the model deployment and post-processing compilation are completed on the Xavier vehicle chip, achieving a running speed of 34 frames per second at most, and the subjective evaluation experience of the fatigue monitoring system is significantly improved.

A Deep Learning Approach for Fatigue Prediction in Sports Using GPS Data and Rate of Perceived Exertion

Monitoring players’ fatigue is essential to maintaining the best performance of players during sports games. The level of fatigue can be measured by the external workload, the aggregated amount of physical activity or internal workload, which is an individual’s psycho-physiological response to that activity. There have been a growing number of studies focusing on the relationship between external and internal workloads for efficient fatigue monitoring. However, they utilize aggregated features to represent the external workload, losing raw data details such as sequential information. This study proposes a deep learning algorithm to predict Rate of Perceived Exertion (RPE) from players’ movement data instead of aggregated features. Electronic Performance and Tracking Systems (EPTS) powered by GPS sensors collected players’ movement data and the RPE from training and match sessions during a Korean professional soccer team season. We preprocessed the raw GPS data to obtain linear and angular components of velocity, acceleration, and jerk. Our proposed model, named FatigueNet, effectively predicted the RPE with mean absolute error (MAE) = 0.8494 ± 0.0557 and root mean square error (RMSE) = 1.2166 ± 0.0737 using the preprocessed movement features. To interpret the predictions of the FatigueNet, we also performed regression activation mapping to localize the discriminative time intervals that contributed more to the prediction results. Our experimental results imply the possibility of automated and objective fatigue monitoring systems based on deep learning instead of arduous manual data collection from players.

Method of biocontrol of vehicle driver fatigue

This article describes an example of negligence of drivers transporting passengers and methods of solving it using modern inventions. One of these troubles is driving a car and moving passengers by taxi driver in a tired state. Since not every driver can correctly assess their psycho-physical condition, so to do this, scientists began to create devices for tracking human behavior when he drives vehicle. The purpose of implementing driver fatigue monitoring systems is to ensure road safety and preserve lives and property of citizens. The use of these systems is to facilitate the work of emergency services and taxi company owners, taxi drivers and their passengers. In our article we want to touch on the problem of overwork, specifically taxi drivers, since their work activity is socially significant and non-compliance with the norms of work and rest periods can lead to tragic consequences. Modern taxi drivers often rely on a strong body of car and electronic gadgets in an unexpected situation on the road. Therefore, when driving a car, despite being overworked, they allow themselves to relax beyond the limit and dont react in time if an emergency occurs. We have studied options for implementing driver fatigue monitoring systems and offer to install them on a taxi car.