Fatigue Mitigation Research Articles

Exploring the role of cardiac activity in forecasting cognitive fatigue with machine learning

Fatigue poses significant risks to safety, productivity, and overall well-being. Traditional statistical methods have been employed for inferring fatigue-related patterns; however, there is a need for interpretable machine learning approaches that integrate time series prediction capabilities to develop intelligent health systems for fatigue management. This study explored forecasting perception and performance scores associated with fatigue using cardiac activity and heart rate variability (HRV), employing both generalized and personalized models with a 10-minute forecast interval during a cognitively fatiguing task. Participants underwent a two-hour working memory task while providing subjective fatigue responses every 10 min, serving as perception labels, and their performance within each 10-minute interval was quantified as their performance labels. The results revealed that performance-labeled models generated lower mean absolute errors compared to perception-labeled models, while the Gradient Boosting Regression algorithm achieved the lowest mean absolute error in forecasting performance scores due to fatigue for both generalized and personalized models. Sample entropy, ratio of Standard Deviation-Poincaré, the proportion of peak-to-peak intervals over 50 ms, coefficient of variation of peak-to-peak intervals, and low frequency were the most important features for predicting performance. These findings offer the potential to forecast performance decline resulting from fatigue in working memory tasks, facilitating the implementation of fatigue mitigation interventions to reduce injury risks and performance impairments. The integration of interpretable machine learning methods with time series forecasting provides valuable insights for developing intelligent systems that proactively manage fatigue and optimize performance across various domains.

Leveraging AI technology for distinguishing Eucommiae Cortex processing levels and evaluating anti-fatigue potential

Eucommiae Cortex (ECO) is a traditional medicinal and edible plant endemic to China, highly prized for its numerous health benefits. It typically undergoes special processing before application. The efficacy of ECO is influenced by processing techniques, necessitating the assurance of stability and consistency in its effects. However, existing methods for identifying ECO are cumbersome, thus, there is an urgent need to develop an accurate, rapid, and non-invasive assessment method. Deep learning techniques employing ResNet and Vision Transformer (ViT) models were employed to classify ECO images at various processing levels. Concurrently, the anti-fatigue properties of ECO were assessed through swimming time, pole climbing experiments, and biochemical analyses including SDH, LDH, ATP content, Na+-K+-ATPase, and Ca2+-Mg2+-ATPase indices. We demonstrated the efficacy of using image analysis to automatically classify ECO with a high degree of accuracy. The results indicated that the Vision Transformer model performed exceptionally well, achieving an accuracy rate exceeding 95 % in grading ECO images. Additionally, our study revealed that mice treated with moderately processed ECO displayed enhanced fatigue mitigation compared to other processing levels, as evidenced by multiple assessments.

Benefits of a group-based running session on feelings of energy and fatigue: No augmenting effect of green exercise during the defoliation season

This randomized controlled trial investigated the impact of a brief session of group-based running on feelings of energy and fatigue, comparing exercise in a local nature reserve versus an outdoor built environment, and a sedentary control condition. Sixty-six physically active university students participated, with 56 providing full data for each time point. Consistent with previous research, participants in both exercise groups reported significantly increased energy and decreased fatigue compared to those in the sedentary control group. However, the type of outdoor environment did not significantly influence the magnitude of improvements. Contrary to expectations, there was no significant difference in energy enhancement or fatigue mitigation between participants who jogged in a local nature reserve versus those who jogged on a cinder track in an urbanized area. The study discusses potential reasons for this, including seasonal variations in forest volatile organic compounds (FVOCs) concentrations, which may impact the psychological effects associated with forest environments. Further research is needed to better understand the complex relationship between exercise, environment, and feelings of energy/feelings of fatigue.

Usage of an AI-Based Password Tool: Impacts of Security Fatigue, Age, and Individual Differences

Effective usage of AI-powered cybersecurity tools may be reduced by users’ tendencies to minimize effort, especially when they are fatigued. The current study identified multiple factors impacting user interaction with DeepPasswd, a tool utilizing deep learning to enhance password strength. Data were obtained from a demanding but monotonous simulated work task requiring regular password updating. Younger users were less likely than older ones to use the tool. Individual differences in anxiety, trust and fatigue were associated with frequency of password tweaks while using the tool, taken as index of engagement. Data on work task performance suggested users vary in their willingness to apply effort. Mitigation of tool neglect and security fatigue requires greater understanding of how users tradeoff effort against enhanced security.

Sleep, Wellness, and Mood of Personnel Standing Watch on US Navy Information Warfare Watchfloors

This project aimed to improve the sleep, wellness, and mood of personnel standing watch on US Navy Information Warfare watchfloors. Data were collected from 82 participants on two US Navy watchfloors. Volunteers wore ŌURA rings and completed questionnaires at the beginning and end of the study. Our findings suggest that the 6-section/8-hour-shift watchbill is generally preferable to the 4-section/12-hour-shift watchbill. We developed general guidelines and recommendations for fatigue mitigation based on the data collected from the two watchfloors, the predicted effectiveness of the two watchbills calculated using the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE-FAST) model, and background literature on shiftwork. The recommendations for watchstanders focused on sleep hygiene practices, sleep environment, timing of sleep and naps, light management, the use of caffeine, alcohol, and nicotine, nutrition, and exercise.

Transcutaneous cervical vagus nerve stimulation enhances second-language vocabulary acquisition while simultaneously mitigating fatigue and promoting focus

Transcutaneous vagus nerve stimulation (tVNS) is a promising technique for enhancing cognitive performance and skill acquisition. Yet, its efficacy for enhancing learning rate and long-term retention in an ecologically valid learning environment has not been demonstrated. We conducted two double-blind sham-controlled experiments examining the efficacy of auricular tVNS (taVNS: Experiment (1) and cervical tVNS (tcVNS: Experiment (2), on a 5 day second-language vocabulary acquisition protocol among highly selected career linguists at the US Department of Defense’s premier language school. tcVNS produced accelerated recall performance during training (Day 2–4), benefits of which were maintained across a 24 h retention interval with no stimulation at the final test. Consistent with prior work, tcVNS also produced fatigue-mitigating and focus-promoting effects as measured by the Air Force Research Laboratory Mood Questionnaire. Based on the current and the previous findings supporting tVNS’ efficacy on performance, training enhancement, and fatigue mitigation, we believe tcVNS to be an effective learning acceleration tool that can be utilized at language-teaching and other institutions focused on intensive training of cognitive skills.

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.

Evaluating the effectiveness of strategies for mitigating fatigue among construction workers in Abuja, Nigeria: a quantitative analysis

Fatigue is one of the menaces that contribute to the rising number of construction-related accidents and fatalities in projects. Therefore, the purpose of this study is to identify several strategies that can be adopted to mitigate fatigue in construction projects. This was achieved through a quantitative study. A questionnaire was the main instrument for data collection in the quantitative study. The study revealed that frustration/depression or work pressure is one of the significant causes of fatigue in construction projects. Fatigue risk management education and bonding among workers are some of the underlying strategies that can be used to mitigate the identified causes. Depending on the nature of the fatigue causative elements, the study further established that some mitigation strategies are more effective than others. Therefore, further studies should be conducted by prospective researchers on a range of attributes that may influence the success of fatigue mitigation strategies in construction projects.

A conceptual examination of an additive manufactured high-ratio coaxial gearbox

This research introduces the novel “Kraken-Gear” mechanism, emphasizing the advantages of additive polymer 3D printing in high-ratio gearbox systems for lightweight robotic applications, such as surgical instruments. The innovative kinematic solution provides high torsional system stiffness, substantial gear ratios, and backlash-free transmission. Leveraging the “hot lithography” additive manufacturing method ensures precise and warp-free gearbox components. Targeting medical technology, the gearbox meets stringent requirements: backlash-free, minimal vibration, high precision, and torque, with minimized weight for ergonomic comfort and fatigue mitigation. Computational simulations assess forces and stresses, highlighting the potential of additive manufacturing for cost-effective and functionally efficient gearbox fabrication. Nevertheless, careful material selection remains imperative for optimal functionality, especially in demanding medical applications. In summary, this research underscores a promising approach to gearbox fabrication, emphasizing the critical role of material selection and simulation-based assessments for optimal performance.

SEAHOWL: Partitioned Multiphysics and Multifidelity Modelling of Wind Turbines with Monolithically Coupled Elastodynamics

We introduce SEAHOWL (Servo-Elasto-Aero-Hydro Offshore Wind Lab), a novel multiphysics multifidelity simulation framework for large-scale onshore, offshore, and floating wind turbines. For structural dynamics, multibody and finite element problems are coupled monolithically and solved within a single system of equations through Project Chrono, providing high numerical stability and optimal coupling accuracy. Combined with partitioned multiphysics, modularity is at the heart of the framework with various numerical methods and solvers (internal or external) available for each physics. This flexibility allows for the selection a target trade-off between numerical accuracy and computational efficiency on a use-case basis. Simulations showcased here have been selected through the prism of industrial R&D challenges, covering: controller response and loads over the operational wind range, 3P effect for different levels of fidelity for the blades, floating wind turbine response under irregular waves and turbulent wind, and innovative multibody application for 3P fatigue mitigation. SEAHOWL is cross-validated against the reference OpenFAST whole-turbine simulator from NREL, showing overall good agreement while differences between the two frameworks are highlighted.

Mitigating Alert Fatigue in Cloud Monitoring Systems: A Machine Learning Perspective

Next generation networks will be largely based on monitoring and telemetry tools that are essential for maintaining optimal performance, ensuring security, managing costs, and performing fault detection and resolution. An integral part of the overall monitoring strategy is alerting, which provides administrators with the necessary information to proactively or reactively manage and optimize network services. However, when monitoring systems generate an excessive number of alerts, many of which may not be actionable or may not represent critical issues, the phenomenon of alert fatigue occurs. Alert fatigue refers to a situation where the volume and the speed of the continuous influx of alerts becomes so overwhelming that the network administrators become desensitized and do not respond to them. To this end, and inspired by recent trends in network automation, where human intervention tends to be minimized, we introduce an alert fatigue mitigation mechanism in monitoring focusing on cloud computing infrastructures. In particular, a composite machine learning methodology is proposed in order to select which alerts will be hidden and which ones will be presented to the administrators. Additionally, to personalize the results, the proposed approach considers the level of users’ experience along with the alert features to further optimize the accuracy of the alert filtering mechanism. The research has been conducted in a realistic environment of a leading monitoring enterprise, Netdata, which provided two datasets for testing our approach. Furthermore, the attained results of the filtering mechanism were evaluated by expert engineers of the company that verified the output of the proposed framework. Specifically, the outcomes confirm that our proposed methodology mitigates the alert fatigue problem with an accuracy that surpass 90% in most cases.

Association of Fatigue Level and Physical Activity among University Students: A Cross-Sectional Survey

Background: Fatigue is a common complaint among adults worldwide, with an estimated 20% experiencing persistent symptoms. Physical activity is recognized as a beneficial behavior with the potential to alleviate feelings of fatigue and enhance energy levels. However, the relationship between physical activity and fatigue among university students remains underexplored. Objective: This study aimed to assess the association between physical activity levels and fatigue among university students, providing insights into the potential of physical activity as a non-pharmacological intervention for fatigue mitigation. Methods: A descriptive cross-sectional study was conducted at the University of Lahore's Islamabad campus, involving 206 participants aged 17 to 25 years. After securing approval from the Institutional Review Committee (IRC)/Ethical Review Board (ERB), data were collected using the Fatigue Assessment Scale (FAS) and the International Physical Activity Questionnaire (IPAQ). The sample size was determined using the Slovin formula, with a confidence interval of 95% and a margin of error of 0.05. Non-probability convenience sampling was employed. Data analysis utilized SPSS version 25, employing Chi-square tests to examine the association between physical activity levels and fatigue. Results: The study comprised 41.3% male and 58.7% female participants. Results indicated that higher levels of physical activity were associated with lower levels of fatigue. Specifically, 38.8% of participants reported no fatigue, 54.9% reported mild to moderate fatigue, and 6.3% reported severe fatigue. Physical activity status revealed that 18.9% of participants had low, 48.1% had moderate, and 33% had high levels of physical activity. The Chi-square test demonstrated a significant association between physical activity levels and fatigue (X² = 23.13, P = 0.00). Conclusion: The findings underscore a significant inverse relationship between physical activity levels and fatigue among university students, suggesting that engaging in higher levels of physical activity may reduce fatigue levels. This study contributes to the understanding of physical activity as a viable approach to fatigue management in young adults.

Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms

Wind energy plays a crucial role in the quest for sustainable energy solutions. However, optimizing the efficiency of wind energy utilization remains a significant challenge. Wake steering, a key strategy in the field, offers the potential to address this challenge. This study introduces an innovative site-specific wake steering framework that incorporates a wake superposition model for wake steering, a machine-learning based fatigue and power predictor and a multi-objective optimizer to both enhance total power generation and mitigate fatigue loads within wind farms. The wake superposition model, developed and validated here, successfully replicates secondary wake steering effects and provides a new solution for calculating superimposed transverse velocity. The study comprehensively considers and implements constraints based on physical laws. Analysis of inflow speed and turbulence levels reveals that wake steering can continue to enhance total power output. Power enhancement can reach up to 18% at lower turbulence levels and still achieve significant increases even when inflow speeds exceed rated values, with only marginal increases in fatigue loads. Lower turbulence levels improve optimization results at the expense of heightened structural loads, while higher turbulence levels lead to diminishing power enhancement and additional fatigue loads. Examination of wind turbine spacing shows that smaller intervals yield substantial power enhancement, with improvements of up to 51.7%, although the effect diminishes as intervals increase and wake recovery takes place. In conclusion, the proposed site-specific wake steering framework offers an efficient means of balancing enhanced wind farm power output and structural integrity, representing a significant advancement in wind energy optimization.

Examining the Relationship Between Nurse Fatigue, Alertness, and Medication Errors.

Working for extended hours in a physically and mentally demanding profession has subjected nurses to occupational fatigue. Limited evidence exists about nurse fatigue and alertness changes throughout shift work and their relationship with medication errors and near misses. The purposes of this study were to: (1) assess the relationship between nurses' fatigue and alertness, (2) evaluate nurses' fatigue and alertness changes throughout their shift, and (3) examine the relationship between nurses' fatigue, alertness, and medication errors and near misses. This prospective study is part of a larger mixed-method study. Fatigue and alertness data from 14 work and non-workdays were collected from a convenience sample of 90 nurses. A wearable actigraph (ReadibandTM) was used to measure alertness, while ecological momentary assessment (EMA) using text messaging was used to measure nurses' fatigue. A 1-unit increase in fatigue was associated with a 1.06-unit reduction in nurses' alertness score (β = -1.06, 95% CI: [-1.33, -0.78], p < .01). Night-shift nurses experienced a 31-point reduction in alertness from the start to the end of the work shift. Nurses' fatigue, but not alertness, was associated with medication errors and near misses (OR = 1.26, 95% CI [1.07, 1.48], p = .01). Initiating fatigue mitigation measures during mid-shift, especially for night-shift nurses, may be a viable option to mitigate fatigue and alertness deterioration among nurses and to maintain patient safety. The multifaceted nature of fatigue, as captured by EMA, is a stronger predictor of medication errors and near misses than device-measured alertness.

The effect of vine tea (Ampelopsis grossedentata) extract on fatigue alleviation via improving muscle mass

Ethnopharmacological relevanceVine Tea (VT, Ampelopsis grossedentata), boasts a venerable tradition in China, with a recorded consumption history exceeding 1200 years. Predominantly utilized by ethnic groups in southwest China, this herbal tea is celebrated for its multifaceted therapeutic attributes. Traditionally, VT has been employed to alleviate heat and remove toxins, exhibit anti-inflammatory properties, soothe sore throats, lower blood pressure, and fortify bones and muscles. In the realm of functional foods derived from plant resources, VT has garnered attention for its potential in crafting anti-fatigue beverages or foods, attributed to its promising efficacy and minimal side effects. Currently, in accordance with the Food Safety Standards set forth by the Monitoring and Evaluation Department of the National Health and Family Planning Commission in China, VT serves as a raw material in various beverages. Aim of the studyVT has an anti-fatigue or similar effect in folk. However, the underlying molecular mechanisms contributing to VT's anti-fatigue effects remain elusive. This study endeavors to investigate the influence of Vine Tea Aqueous Extract (VTE) on fatigue mitigation and to elucidate its operative mechanisms, with the objective of developing VTE as a functional beverage. Materials and methodsThe preparation of VTE involved heat extraction and freeze-drying processes, followed by the identification of its metabolites using UPLC-QTOF-MS to ascertain the chemical composition of VTE. A fatigue model was established using a forced swimming test in mice. Potential molecular targets were identified through network pharmacology, transcriptome analysis, and molecular docking. Furthermore, RT-PCR and Western blot techniques were employed to assess mRNA and protein expressions related to the AMPK and FoxO pathways. ResultsVTE significantly prolonged the duration of swimming time in an exhaustive swimming test in a dose-dependent manner, while simultaneously reducing the concentrations of blood lactic acid (LA), lactate dehydrogenase (LDH), serum urea nitrogen (SUN), and creatine kinase (CK). Notably, the performance of the high-dose VTE group surpassed that of the well-recognized ginsenoside. VTE demonstrated a regulatory effect akin to ginsenoside on the AMPK energy metabolism pathway and induced downregulation in the expression of Gadd45α, Cdkn1a, FOXO1, and Fbxo32 genes, suggesting an enhancement in skeletal muscle mass. These findings indicate that VTE can improve energy metabolism and muscle mass concurrently. ConclusionsVTE exhibits significant anti-fatigue effects, and its mechanism is intricately linked to the modulation of the AMPK and FoxO pathways. Crucially, no caffeine or other addictive substances with known side effects were detected in VTE. Consequently, vine tea shows substantial promise as a natural resource for the development of anti-fatigue beverages within the food industry.

Vitamin D and Its Role on the Fatigue Mitigation: A Narrative Review.

Vitamin D has historically been associated with bone metabolism. However, over the years, a growing body of evidence has emerged indicating its involvement in various physiological processes that may influence the onset of numerous pathologies (cardiovascular and neurodegenerative diseases, rheumatological diseases, fertility, cancer, diabetes, or a condition of fatigue). This narrative review investigates the current knowledge of the pathophysiological mechanisms underlying fatigue and the ways in which vitamin D is implicated in these processes. Scientific studies in the databases of PubMed, Scopus, and Web of Science were reviewed with a focus on factors that play a role in the genesis of fatigue, where the influence of vitamin D has been clearly demonstrated. The pathogenic factors of fatigue influenced by vitamin D are related to biochemical factors connected to oxidative stress and inflammatory cytokines. A role in the control of the neurotransmitters dopamine and serotonin has also been demonstrated: an imbalance in the relationship between these two neurotransmitters is linked to the genesis of fatigue. Furthermore, vitamin D is implicated in the control of voltage-gated calcium and chloride channels. Although it has been demonstrated that hypovitaminosis D is associated with numerous pathological conditions, current data on the outcomes of correcting hypovitaminosis D are conflicting. This suggests that, despite the significant involvement of vitamin D in regulating mechanisms governing fatigue, other factors could also play a role.

Steps toward developing a comprehensive fatigue monitoring and mitigation solution: perspectives from a cohort of United States Naval Surface Force officers.

This study analyzed fatigue and its management in US Naval Surface Force warships, focusing on understanding current practices and barriers, and examining the influence of organizational and individual factors on managing chronic fatigue. Furthermore, this study explored the impact of organizational and individual factors on fatigue management. As part of a larger study, 154 naval officers (mean ± standard deviation; 31.5 ± 7.0 years; 8.8 ± 6.8 years of service; 125 male, and 29 female) completed a fatigue survey. The survey addressed (1) self-reported fatigue, (2) fatigue observed in others, (3) fatigue monitoring strategies, (4) fatigue mitigation strategies, and (5) barriers to fatigue mitigation. Logistic and ordinal regressions were performed to examine the effect of individual (i.e. sleep quality and years in military service) and organizational (i.e. ship-class) factors on fatigue outcomes. Fatigue was frequently experienced and observed by 23% and 54% of officers, respectively. Of note, officers often monitored fatigue reactively (i.e. 65% observed others nodding off and 55% observed behavioral impairments). Still, officers did not frequently implement fatigue mitigation strategies, citing few operationally feasible mitigation strategies (62.3%), being too busy (61.7%), and not having clear thresholds for action (48.7%). Fatigue management varies across organizational factors, which must be considered when further developing fatigue management strategies. Fatigue remains a critical concern aboard surface force ships and it may be better addressed through development of objective sleep and fatigue monitoring tools that could inform leadership decision-making.

Enhancement of Quick-Charge Performance by Fluoroethylene Carbonate additive from the Mitigation of Electrode Fatigue During Normal &amp;lt;italic&amp;gt;C&amp;lt;/italic&amp;gt;-rate Cycling

&lt;p&gt;The quick-charging performance of SiO electrodes is evaluated with a focus on solid electrolyte interphase (SEI)-reinforcing effects. The study reveals that the incorporation of fluoroethylene carbonate (FEC) into the SiO electrode significantly reduced the electrode fatigue, which is from the the viscoelastic properties of the FEC-derived SEI film. The impact of FEC is attributed to its ability to minimize the mechanical failure of the electrode caused by additional electrolyte decomposition. This beneficial outcome arises from volumetric stain-tolerant characteristics of the FEC-derived SEI film, which limited exposure of the bare SiO surface during 0.5 C-rate cycling. Notably, FEC greatly improves Li deposition during quick-charge cycles following aging at 0.5 &lt;italic&gt;C&lt;/italic&gt;-rate cycling due to its ability to maintain a strong electrical connection between active materials and the current collector, even after extended cycling. Given these findings, we assert that mitigating SEI layer deterioration, which compromises the electrode structure, is vital. Hence, enhancing the interfacial attributes of the SiO electrode becomes crucial for maintaining kinetic efficiency of battery system.&lt;/p&gt;

Mobility Aircrew Fatigue Perceptions and Mitigation Strategies

Abstract: Mobility aircrew operate in unique, high-tempo environments resulting in susceptibility to fatigue. In the current study we examined fatigue perceptions and mitigation strategies before and during mission execution with questionnaires from 44 volunteers from a US Air Force air mobility squadron. Results suggest that fatigue is a serious safety-of-flight concern for the community; however, crews generally did not reference the available fatigue risk management (FRM) tool for missions. Aircrew member characteristics were associated with differences in fatigue-related perceptions and mitigation strategies. Crews used various personal fatigue mitigation strategies and had habitual stimulant and depressant consumption during missions based on daily routines. We discuss implications of these findings for FRM tools.

Exploring Cognitive Fatigue: Machine Learning, Forecasting, Generalization, Personalization, Inference and Labels

Fatigue is a feeling of tiredness or weakness that can be physical, mental or both. Interpretable machine learning methods can be used for inference while having the dimension of time series prediction for developing intelligent health systems to manage fatigue. The study explored forecasting perception and performance scores associated with fatigue manifestation using cardiac activity with generalized and personalized models at a forecast interval of 10-minutes during a cognitively fatiguing task. Participants underwent a 2-hour cognitively fatiguing working memory task with subjective fatigue responses obtained every 10-minutes. Participants’ performance was calculated over the 10-minute interval. Performance labeled models had the lowest mean absolute error for forecasting in both generalized and personalized models using Gradient Boosting Regression. This provides the ability to forecast performance decrease due to fatigue and to generate fatigue mitigation interventions to reduce fatigue-related injuries.