Fatigue Monitoring Research Articles

Breaking down the standardised run: additional insights into the monitoring of neuromuscular fatigue

Breaking down the standardised run: additional insights into the monitoring of neuromuscular fatigue

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.

Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

Exploring the usefulness of real-time digitally supported fatigue monitoring in fatigue management: Perspectives from occupational therapists and brain injury survivors

Introduction: Persistent fatigue after acquired brain injury (ABI) needs long-term self-management. Self-monitoring supports self-management and informs the use of fatigue management strategies. Using ecological momentary assessment to monitor fatigue offers a data-driven approach to managing fatigue. Aims: To explore the usefulness of self-monitoring fatigue in real-time, using ecological momentary assessment to support self-management, from the perspective of people with ABI and occupational therapists. Methods: People with ABI monitoried their fatigue by wearing a Fitbit and completing six surveys a day on their phone for 6 days. Think aloud and semi-structured interviews elicited views on self-monitoring and the data generated. Transcripts were analysed using reflexive thematic analysis. Results: Four themes were developed from people with ABI ( n = 9): (1) Attending to experience, (2) making sense of data, (3) the relationship between fatigue and activity, (4) implications for daily life. Three themes from occupational therapists ( n = 5): (1) Challenges of using of data, (2) perceived benefits of self-monitoring, (3) viewing data in relation to their understanding of fatigue. Conclusion: Data generated in real-time challenged perspectives on fatigue and fatigue management. These insights may help people with ABI and their clinicians to plan personalised strategies for fatigue management and evaluate its impact on daily living.

Investigating 24- to 48-hr Forecast of Offshore Worker Alertness and Vigilance Using Multimodal Sources: A Proactive Fatigue Monitoring Paradigm

Investigating 24- to 48-hr Forecast of Offshore Worker Alertness and Vigilance Using Multimodal Sources: A Proactive Fatigue Monitoring Paradigm

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Applying a Holistic Injury Prevention Approach to Elite Triathletes.

(1) Background: Studies on injury prevention programs are lacking for triathletes. The aim of the present study was to describe the results of a holistic (injury) training prevention program (HITP), based on training load control and strength training, in elite triathletes. (2) Methods: The study was conducted over 2021-2023 and involved 18 males and 10 females from the same training group. The HITP itself included various methods of fatigue monitoring, strength training focused on the prevention of overuse injuries (OIs), cycling skills training, and recovery strategies. The total number and type of injuries that were sustained, subsequent training/competition absence time, and injury incidence were determined. (3) Results: Twenty-four injuries were recorded over all three seasons, i.e., 0.65 injuries per 1000 h of training and competition exposure. Fourteen injuries were traumatic injuries (TIs) and ten were OIs. Of the OIs, four were of minimal severity, two were mild, three were moderate, and one was severe (accounting for 1-3, 4-7, 8-28, and >28 days of training absenteeism, respectively). A total of 46.4% of the participants did not present any type of injury and 71,4% did not incur any OIs. Average absenteeism was 17.3 days per injury. (4) Conclusions: The HITP design and implementation resulted in low OI and severe injury incidence. Due to their unpredictable nature, the number of TIs was not reduced. The TIs were suffered more frequently by men. Women are more likely to suffer from OIs, so it is particularly important to prevent OIs in women.

Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sites

The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks.

Illumination Intelligent Adaptation and Analysis Framework: A comprehensive solution for enhancing nighttime driving fatigue monitoring.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.

Wearable heart rate sensing and critical power-based whole-body fatigue monitoring in the field

Whole-body fatigue (WBF) presents a concerning risk to construction workers, which can impact function and ultimately lead to accidents and diminished productivity. This study proposes a new WBF monitoring technique by applying the Critical Power (CP) model, a bioenergetic model, with a wrist-worn heart rate sensor. The authors modified the CP model to calculate WBF from the percentage of heart rate reserve (%HRR) and generated a personalized model via WBF perception surveys. Data were collected for two days from 33 workers at four construction sites. The results showed that the proposed technique can monitor field workers’ perceived WBF with a mean absolute error of 12.8% and Spearman correlation coefficient of 0.83. This study, therefore, demonstrates the viability of wearable WBF monitoring on construction sites to support programs aimed at improving workplace safety and productivity.

Dynamic monitoring of spring strain and fatigue in high voltage circuit breakers

Dynamic monitoring of spring strain and fatigue in high voltage circuit breakers

Fatigue and residual strain monitoring for thermoplastic composite using embedded FBG inscribed in highly‑birefringent side-hole elliptical core optical fiber

Monitoring of thermoplastic carbon reinforced composites is critical for the safe operation and optimization of composite structures. Strain measurement during their manufacturing and operation is often achieved with embedded Fiber Bragg Gratings (FBG). However, conventional FBGs can only measure axial strain. To measure multiaxial strain in thermoplastic composites, FBGs inscribed in highly-birefringent side-hole elliptical core optical fiber (SH2 FBG) are proposed. SH2 FBG were used to monitor composite produced by automated tape laying process, first − to measure residual in-plane and out-of-plane strain during the manufacturing stage, second − in plane transverse strain during cyclic loading. It was demonstrated that the sensors can be used to measure multiaxial residual strain, observe changes in material stiffness, and calculate the area of the mechanical hysteresis loop. As a result − it was shown that embedding SH2 FBG can be an effective and robust method for monitoring the carbon fiber reinforced thermoplastic composite.

A Guided Comparative Analysis of Fatigue Frameworks in Australasian Ambulance Services

Objective Paramedics work in a complex, unpredictable environment, subject to many external stressors including critically unwell patients, dangerous driving conditions, and prolonged shift work. Paramedic fatigue from these and other occupational demands is well documented. Ambulance services attempt to safeguard paramedics from fatigue using internal policies or procedures – a type of Fatigue Risk Management Systems (FRMSs). This study reviews ambulance service fatigue frameworks to understand the current situation in fatigue management in paramedicine, and to identify fatigue monitoring tools, strategies, and other components of these frameworks that are designed to protect personnel. Methods This study involved a qualitative document thematic content analysis. All eleven statutory ambulance services across Australia, New Zealand, and Papua New Guinea, represented by the Council of Ambulance Authorities, were contacted and invited to participate. Fatigue frameworks were collated and entered into NVivo where data extraction occurred through three a priori areas (fatigue, fatigue mitigation tools & fatigue management). Results Nine of the eleven ambulance services provided fatigue documentation, with one declining to participate, and one did not respond to invitations. Through thematic analysis and abstraction, seven themes were identified: fatigue definition, consequences of fatigue, sources of fatigue, signs and symptoms of fatigue, fatigue-related incidents, fatigue monitoring tools, and fatigue mitigation. There was also poor alignment between provided frameworks and established FRMSs components. Conclusion Our findings provide an initial insight into existing ambulance service fatigue frameworks across Australia, New Zealand, and Papua New Guinea. The many inconsistencies in frameworks between ambulance services highlight an opportunity to develop a more consistent, collaborative approach that follows evidence-based FRMSs guidelines.

Recognizing and predicting muscular fatigue of biceps brachii in motion with novel fabric strain sensors based on machine learning

Localized muscle fatigue is of major kinematic and medical significance in the field of sports and rehabilitations. However, only few works reported fatigue recognition for field training, and via analysis of only the noise-susceptible electromyography. In this study, along with portable EMG sensors, a wearable smart arm band powered by fabric strain sensors was implemented to detect the thickness variation of biceps. Dumbbell curl training protocol was designed, and 32 subjects were enrolled, contributing to a database of 580 curl samples. Fatigue-related characteristics of muscle thickness were proposed according to muscle physiology and combined with that of sEMG signals, forming feature vectors of the samples. Clustering results showed that the fatigued cycles can be successfully recognized and labeled. For potential applications, fatigue prediction models based on supervised learning methods were further proposed and compared, of which the SVM-based model was observed with satisfactory overall accuracy of 83.3%, yet the fatigue samples can be predicted at much higher rate, with effective recall 90%, F1-SCORE 95% and AUC 98.7%. This work not only exploits feasibility of using latent physiological indicators such as muscle thickness in muscle fatigue monitoring, the result and methodologies will also inspire wider horizon of human-centered applications using novel flexible sensors.

Cancer-related Fatigue and the Additive Effect of Treatment in the Context of Lymphoma: An Analysis of the Lymphoma Coalition's 2022 Global Patient Survey.

CRF is a highly incident phenomenon in lymphoma that can be ascribed to a combination of causes. We have demonstrated substantial variability across various subtypes of lymphoma and have estimated that nearly half of the reported fatigue comes from treatment. Increased screening for and monitoring of fatigue will yield favorable health-related quality of life that will benefit health technology assessment activities and yield improved outcomes for patients.

Subjective and Objective Monitoring Markers: Are They Related to Game Performance Indicators in Elite Female Volleyball Players?

This cohort study aimed to investigate the relationship between subjective (wellness and internal training load [ITL]) and objective (neuromuscular fatigue) monitoring markers and performance aspects (reception quality [RQ] and attack efficiency [AE]) in professional female volleyball players. The study was conducted over an 8-week period during the final mesocycle of the competitive phase. A total of 24 training sessions and 10 matches were included in the analysis. Subjective measures of wellness and ITL were assessed, and neuromuscular fatigue was evaluated using countermovement-jump (CMJ) height. RQ and AE were determined based on game statistics. The study found a positive relationship between wellness and RQ, particularly affecting outside hitters and liberos. ITL showed a positive association with AE, primarily impacting outside hitters, opposite hitters, and middle blockers. Additionally, ITL demonstrated a negative correlation with RQ, mainly affecting outside hitters and liberos. CMJ performance was associated with AE, where a decrease in CMJ height was linked to reduced AE. The findings highlight the importance of considering players' wellness scores in training and match strategies for different positions. Careful management of training loads, considering both physical and technical demands, is crucial for optimizing performance outcomes. Monitoring neuromuscular fatigue, as indicated by CMJ performance, is particularly relevant for outside hitters, opposite hitters, and middle blockers involved in attack actions. Coaches, trainers, and sports practitioners can use these insights to develop position-specific training protocols and implement effective strategies for maintaining or improving performance metrics under various stressors.

Validation of a model-based dual-band modal decomposition and expansion approach for fatigue monitoring of offshore wind turbine foundations

The fatigue limit state strongly drives the lifetime of offshore wind turbine support structures. Despite steady advancements in their design, the accumulation of fatigue damage over the lifetime of the structure remains uncertain; one way to improve certainty is to directly monitor structural loads at fatigue-critical locations, e.g., near or below the mudline. However, directly monitoring the loads at these locations is often not possible, since these locations do not allow for easy installation or retrofitting of sensors. To this end, model-based virtual sensing techniques can be employed to estimate the structural response at unmeasured locations based on vibration response data and a finite element model which can accurately describe the modal and operational deflection shapes of a specific turbine [1,2,3]. For this contribution, a modal decomposition and expansion (MDE) algorithm [4] will be used to predict strain time series at fatigue-critical locations, based on acceleration and strain data obtained from the tower of an operational OWT. Emphasis will be put on sparse data situations in which a realistic amount of sensor data is used as input. Fibre-optical strain gauges close to and below the mudline level (Fig 1) will be used for direct validation of the extrapolation capabilities. The required finite element model is built using an in-house developed and verified integrated FE model class [5,6], in combination with a unique database which contains all available geotechnical and structural data of offshore wind turbines (owimetadatabase) [7,8]. This combination allows us to generate fleet-wide, turbine-specific, finite elements models for several wind farms. The results will be presented in terms of time series (e.g., Fig. 2) and fatigue damage-related metrics. Preliminary results show that strain time series estimated with the MDE method show good agreement with the validation data.

Towards a more individualised assessment of post-training fatigue in young footballers

Despite improvements in training load and fatigue monitoring, fatigue status may adversely affect intrinsic risk factors, particularly in young footballers. This study aimed to better individualise the fatigue effects of a 75-min football training session in young elite male players.Eighteen players (15.6 ± 1.7 years) completed a test battery before and after a football training session. Assessments included capillary blood samples (HCO3−, lactate, pH), subjective ratings of muscle soreness and fatigue. Functional tests included 30 m field sprint, laboratory drop jump (DJ) and horizontal force-velocity (H-FvP). The k-means method was applied to the first two dimensions of principal component analysis of changes in the H-FvP and DJ tests.Football training resulted in significant physiological changes and functional impairments, in particular an increase in interlimb asymmetry in the DJ test. No significant fatigue effect was found on the H-FvP test data. However, confirming the interest of combining the two tests, cluster analysis revealed two subgroups: In the H-FvP test, Cluster 1 decreased in V‾0 (p < 0.001) and P‾max (p < 0.01), while Cluster 2 decreased in F‾0 (p < 0.001). In the DJ test, Cluster 1 decreased in mean velocity (p < 0.01), relative mean and maximum power (p < 0.01) during push-off, while Cluster 2 decreased in relative mean push-off force (p < 0.01) and increased in interlimb asymmetry during braking (p < 0.01).This study highlights the contribution and complementarity of the H-FvP and DJ tests to improve individual screening for fatigue-related functional changes in footballers. Extrapolated values from the H-FvP test led to the identification of two subgroups with opposite fatigue profiles. One subgroup showed increased interlimb asymmetry in DJ, indicating an increased risk of injury with fatigue. These findings highlight the need for individualised fatigue assessment in young footballers.

Normalization of rapid force to peak force in an isometric hamstring assessment using force plates

PurposeThe aim of this investigation was to determine the reliability of normalizing rapid force (RF) production to peak force assessed during an isometric knee flexor assessment, and to present a novel method of classifying athletes' potential training needs within the 90–90 isometric hamstring assessment. ProceduresTwenty elite female soccer players (age: 20.7 ± 4.7 years; height: 168.2 ± 5.5 cm; body mass: 62.8 ± 7.0 kg), with no recent (>6 months) history of hamstring strain injury, volunteered to participate in the study. Following a standardized warm-up, each participant performed three maximal isometric hamstring contractions, with their heel resting on a force plate, elevated on a box, to ensure that their hips and knees were at 90° Data was analyzed to determine peak for (PF), RF was established as force expressed at 100 ms (F100) and force expressed at 200 ms (F200), with force at each time-point subsequently normalized to a percentage of PF. FindingsF100 and F200 normalized to PF demonstrated good absolute reliability (%CV = 6.12–7.62) and moderate relative reliability (ICC = 0.689–0.703). Concurrently observing PF and normalized F100 and F200 could provide clear training and monitoring goals. ConclusionsNormalizing measures of RF production, including F100 and F200, to PF can be performed reliability. Therefore, could be tracked overtime to identify changes as an effect of training or for fatigue monitoring purposes. However, further research is required to determine how knee flexor force-time characteristics change in relation to focused training and how these characteristics change in response to fatiguing activities.

Mental fatigue recognition study based on 1D convolutional neural network and short-term ECG signals.

Mental fatigue has become a non-negligible health problem in modern life, as well as one of the important causes of social transportation, production and life accidents. Fatigue detection based on traditional machine learning requires manual and tedious feature extraction and feature selection engineering, which is inefficient, poor in real-time, and the recognition accuracy needs to be improved. In order to recognize daily mental fatigue level more accurately and in real time, this paper proposes a mental fatigue recognition model based on 1D Convolutional Neural Network (1D-CNN), which inputs 1D raw ECG sequences of 5s duration into the model, and can directly output the predicted fatigue level labels. The fatigue dataset was constructed by collecting the ECG signals of 22 subjects at three time periods: 9:00-11:00 a.m., 14:00-16:00 p.m., and 19:00-21:00 p.m., and then inputted into the 19-layer 1D-CNN model constructed in the present study for the classification of mental fatigue in three grades. The results showed that the model was able to recognize the fatigue levels effectively, and its accuracy, precision, recall, and F1 score reached 98.44%, 98.47%, 98.41%, and 98.44%, respectively. This study further improves the accuracy and real-time performance of recognizing multi-level mental fatigue based on electrocardiography, and provides theoretical support for real-time fatigue monitoring in daily life.