Flexible sensor-based biomechanical evaluation of low-back exoskeleton use in lifting.

This study aimed to establish an ambulatory field-friendly system based on miniaturised wireless flexible sensors for studying the biomechanics of human-exoskeleton interactions. Twelve healthy adults performed symmetric lifting with and without a passive low-back exoskeleton, while their movements were tracked using both a flexible sensor system and a conventional motion capture (MoCap) system synchronously. Novel algorithms were developed to convert the raw acceleration, gyroscope, and biopotential signals from the flexible sensors into kinematic and dynamic measures. Results showed that these measures were highly correlated with those obtained from the MoCap system and discerned the effects of the exoskeleton, including increased peak lumbar flexion, decreased peak hip flexion, and decreased lumbar flexion moment and back muscle activities. The study demonstrated the promise of an integrated flexible sensor-based system for biomechanics and ergonomics field studies as well as the efficacy of exoskeleton in relieving the low-back stress associated with manual lifting.

This study established and tested a flexible sensor-based ambulatory system for biomechanical evaluation of human-exoskeleton interactions and as a promising new tool for field ergonomics studies in practical or naturalistic settings.Abbreviations: MoCap: motion capture; WMSD: Work-related musculoskeletal disorders; EMG: electromyography; IMU: inertial measurement unit; TES: thoracic erector spinae; LES: lumbar erector spinae; WITH: tasks performed with wearing the exoskeleton; WITHOUT: tasks performed without wearing the exoskeleton; RMS: root mean square; RMSE: root-mean-square error; r: Pearson's correlation coefficient; ASIS: anterior superior iliac spine.

Sex-specific Neural Strategies During Fatiguing Work in Older Adults.

BACKGROUND: Historical biases in ergonomics-related studies have been attributed to lack of participant diversity and sensitivity of measurements to capture variability between diverse groups. We posit that a neuroergonomics approach, that is, study of brain-behavior relationships during fatiguing work, allows for unique insights on sex differences in fatigue mechanisms that are not available via traditional "neck down" measurement approaches. OBJECTIVE: This study examined the supraspinal mechanisms of exercise performance under fatigue and determined if there were any sex differences in these mechanisms. METHODS: Fifty-nine older adults performed submaximal handgrip contractions until voluntary fatigue. Traditional ergonomics measures, namely, force variability, electromyography (EMG) of arm muscles, and strength and endurance times, and prefrontal and motor cortex hemodynamic responses were recorded. RESULTS: There were no significant differences observed between older males and females in fatigability outcomes (i.e., endurance times, strength loss, and EMG activity) and brain activation. Effective connectivity from prefrontal to motor areas was significant for both sexes throughout the task, but during fatigue, males had higher interregional connectivity than females. DISCUSSION: While traditional metrics of fatigue were comparable between the sexes, we observed distinct sex-specific neuromotor strategies (i.e., information flow between frontal-motor regions) that were adopted by older adults to maintain motor performance. APPLICATION: The findings from this study offer insights into the capabilities and adaptation strategies of older men and women under fatiguing conditions. This knowledge can facilitate in the development of effective and targeted ergonomic strategies that accommodate for the varying physical capacities of diverse worker demographics.

Neurophysiological, muscular, and perceptual adaptations of exoskeleton use over days during overhead work with competing cognitive demands.

This study captured neurophysiological, muscular, and perceptual adaptations to shoulder exoskeleton use during overhead work with competing physical-cognitive demands. Twenty-four males and females, randomly divided into control and exoskeleton groups, performed an overhead reaching and pointing task over three days without (single task) and with (dual task) a working memory task. Task performance, electromyography (EMG), neural activity, heart rate, and subjective responses were collected. While task completion time reduced for both groups at the same rate over days, EMG activity of shoulder muscles was lower for the exoskeleton group for both tasks, specifically for females during the dual task. Dual task reduced the physiological benefits of exoskeletons and neuromotor strategies to adapt to the dual task demands differed between the groups. Neuromuscular benefits of exoskeleton use were immediately realized irrespective of cognitive demand, however the perceptual, physiological, and neural adaptations with exoskeleton use were task- and sex-specific.

Understanding Patient Beliefs in Using Technology to Manage Diabetes: Path Analysis Model From a National Web-Based Sample.

BACKGROUND:  With 425 million individuals globally living with diabetes, it is critical to support the self-management of this life-threatening condition. However, adherence and engagement with existing technologies are inadequate and need further research. OBJECTIVE:  The objective of our study was to develop an integrated belief model that helps identify the significant constructs in predicting intention to use a diabetes self-management device for the detection of hypoglycemia. METHODS:  Adults with type 1 diabetes living in the United States were recruited through Qualtrics to take a web-based questionnaire that assessed their preferences for a device that monitors their tremors and alerts them of the onset of hypoglycemia. As part of this questionnaire, a section focused on eliciting their response to behavioral constructs from the Health Belief Model, Technology Acceptance Model, and others. RESULTS:  A total of 212 eligible participants responded to the Qualtrics survey. Intention to use a device for the self-management of diabetes was well predicted (R2=0.65; F12,199=27.19; P<.001) by 4 main constructs. The most significant constructs were perceived usefulness (ß=.33; P<.001) and perceived health threat (ß=.55; P<.001) followed by cues to action (ß=.17; P<.001) and a negative effect from resistance to change (ß=-.19; P<.001). Older age (ß=.025; P<.001) led to an increase in their perceived health threat. CONCLUSIONS: For individuals to use such a device, they need to perceive it as useful, perceive diabetes as life-threatening, regularly remember to perform actions to manage their condition, and exhibit less resistance to change. The model predicted the intention to use a diabetes self-management device as well, with several constructs found to be significant. This mental modeling approach can be complemented in future work by field-testing with physical prototype devices and assessing their interaction with the device longitudinally.

Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes.

BACKGROUND: Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors. OBJECTIVE: In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data. METHODS: We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states. RESULTS: The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth. CONCLUSIONS: Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.

Modeling Brain Dynamics During Virtual Reality-Based Emergency Response Learning Under Stress.

BACKGROUND: Stress affects learning during training, and virtual reality (VR) based training systems that manipulate stress can improve retention and retrieval performance for firefighters. Brain imaging using functional Near Infrared Spectroscopy (fNIRS) can facilitate development of VR-based adaptive training systems that can continuously assess the trainee's states of learning and cognition. OBJECTIVE: The aim of this study was to model the neural dynamics associated with learning and retrieval under stress in a VR-based emergency response training exercise. METHODS: Forty firefighters underwent an emergency shutdown training in VR and were randomly assigned to either a control or a stress group. The stress group experienced stressors including smoke, fire, and explosions during the familiarization and training phase. Both groups underwent a stress memory retrieval and no-stress memory retrieval condition. Participant's performance scores, fNIRS-based neural activity, and functional connectivity between the prefrontal cortex (PFC) and motor regions were obtained for the training and retrieval phases. RESULTS: The performance scores indicate that the rate of learning was slower in the stress group compared to the control group, but both groups performed similarly during each retrieval condition. Compared to the control group, the stress group exhibited suppressed PFC activation. However, they showed stronger connectivity within the PFC regions during the training and between PFC and motor regions during the retrieval phases. DISCUSSION: While stress impaired performance during training, adoption of stress-adaptive neural strategies (i.e., stronger brain connectivity) were associated with comparable performance between the stress and the control groups during the retrieval phase.

Physiological and perceptual consequences of trust in collaborative robots: An empirical investigation of human and robot factors.

Measuring trust is an important element of effective human-robot collaborations (HRCs). It has largely relied on subjective responses and thus cannot be readily used for adapting robots in shared operations, particularly in shared-space manufacturing applications. Additionally, whether trust in such HRCs differ under altered operator cognitive states or with sex remains unknown. This study examined the impacts of operator cognitive fatigue, robot reliability, and operator sex on trust symptoms in collaborative robots through both objective measures (i.e., performance, heart rate variability) and subjective measures (i.e., surveys). Male and female participants were recruited to perform a metal surface polishing task in partnership with a collaborative robot (UR10), in which they underwent reliability conditions (reliable, unreliable) and cognitive fatigue conditions (fatigued, not fatigued). As compared to the reliable conditions, unreliable robot manipulations resulted in perceived trust, an increase in both sympathetic and parasympathetic activity, and operator-induced reduction in task efficiency and accuracy but not precision. Cognitive fatigue was shown to correlate with higher fatigue scores and reduced task efficiency, more severely impacting females. The results highlight key interplays between operator states of fatigue, sex, and robot reliability on both subjective and objective responses of trust. These findings provide a strong foundation for future investigations on better understanding the relationship between human factors and trust in HRC as well as aid in developing more diagnostic and deployable measures of trust.

Trust in Shared-Space Collaborative Robots: Shedding Light on the Human Brain.

BACKGROUND: Industry 4.0 is currently underway allowing for improved manufacturing processes that leverage the collective advantages of human and robot agents. Consideration of trust can improve the quality and safety in such shared-space human-robot collaboration environments. OBJECTIVE: The use of physiological response to monitor and understand trust is currently limited due to a lack of knowledge on physiological indicators of trust. This study examines neural responses to trust within a shared-workcell human-robot collaboration task as well as discusses the use of granular and multimodal perspectives to study trust. METHODS: Sixteen sex-balanced participants completed a surface finishing task in collaboration with a UR10 collaborative robot. All participants underwent robot reliability conditions and robot assistance level conditions. Brain activation and connectivity using functional near infrared spectroscopy, subjective responses, and performance were measured throughout the study. RESULTS: Significantly, increased neural activation was observed in response to faulty robot behavior within the medial and right dorsolateral prefrontal cortex (PFC). A similar trend was observed for the anterior PFC, primary motor cortex, and primary visual cortex. Faulty robot behavior also resulted in reduced functional connectivity strengths throughout the brain. DISCUSSION: These findings implicate regions in the prefrontal cortex along with specific connectivity patterns as signifiers of distrusting conditions. The neural response may be indicative of how trust is influenced, measured, and manifested for human-robot collaboration that requires active teaming. APPLICATION: Neuroergonomic response metrics can reveal new perspectives on trust in automation that subjective responses alone are not able to provide.

A Window Into the Tired Brain: Neurophysiological Dynamics of Visuospatial Working Memory Under Fatigue.

OBJECTIVE: We examine the spatiotemporal dynamics of neural activity and its correlates in heart rate and its variability (HR/HRV) during a fatiguing visuospatial working memory task. BACKGROUND: The neural and physiological drivers of fatigue are complex, coupled, and poorly understood. Investigations that combine the fidelity of neural indices and the field-readiness of physiological measures can facilitate measurements of fatigue states in operational settings. METHOD: Sixteen healthy adults, balanced by sex, completed a 60-minute fatiguing visuospatial working memory task. Changes in task performance, subjective measures of effort and fatigue, cerebral hemodynamics, and HR/HRV were analyzed. Peak brain activation, functional and effective connections within relevant brain networks were contrasted against spectral and temporal features of HR/HRV. RESULTS: Task performance elicited increased neural activation in regions responsible for maintaining working memory capacity. With the onset of time-on-task effects, resource utilization was seen to increase beyond task-relevant networks. Over time, functional connections in the prefrontal cortex were seen to weaken, with changes in the causal relationships between key regions known to drive working memory. HR/HRV indices were seen to closely follow activity in the prefrontal cortex. CONCLUSION: This investigation provided a window into the neurophysiological underpinnings of working memory under the time-on-task effect. HR/HRV was largely shown to mirror changes in cortical networks responsible for working memory, therefore supporting the possibility of unobtrusive state recognition under ecologically valid conditions. APPLICATIONS: Findings here can inform the development of a fieldable index for cognitive fatigue.

Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review.

The degree of successful human-robot collaboration is dependent on the joint consideration of robot factors (RF) and human factors (HF). Depending on the state of the operator, a change in a robot factor, such as the behavior or level of autonomy, can be perceived differently and affect how the operator chooses to interact with and utilize the robot. This interaction can affect system performance and safety in dynamic ways. The theory of human factors in human-automation interaction has long been studied; however, the formal investigation of these HFs in shared space human-robot collaboration (HRC) and the potential interactive effects between covariate HFs (HF-HF) and HF-RF in shared space collaborative robotics requires additional investigation. Furthermore, methodological applications to measure or manipulate these factors can provide insights into contextual effects and potential for improved measurement techniques. As such, a systematic literature review was performed to evaluate the most frequently addressed operator HF states in shared space HRC, the methods used to quantify these states, and the implications of the states on HRC. The three most frequently measured states are: trust, cognitive workload, and anxiety, with subjective questionnaires universally the most common method to quantify operator states, excluding fatigue where electromyography is more common. Furthermore, the majority of included studies evaluate the effect of manipulating RFs on HFs, but few explain the effect of the HFs on system attributes or performance. For those that provided this information, HFs have been shown to impact system efficiency and response time, collaborative performance and quality of work, and operator utilization strategy.

Mind over body: A neuroergonomic approach to assessing motor performance under stress in older adults.

Stress impairs motor performance, which is exacerbated with age. Stress also impairs brain activity in the prefrontal cortex, which communicates with the motor areas of the brain to regulate exercise and motor performance. To develop ergogenic strategies for the aging workforce, mind (brain)-body mechanisms behind the effect of stress on neuromuscular performance need to be well understood. This study investigated the influence of social stress on motor performance and information flow between the frontal and motor regions of the brain during intermittent handgrip contractions among older adults. Thirty older adults, balanced by gender, performed intermittent handgrip contractions at 30% of maximum strength before and after being subjected to a social stressor. Force steadiness, strength loss, root mean square electromyogram (EMG) activity, activation of the brain regions, and functional and effective connectivity between the frontal and motor brain regions were computed for pre- and post-stressor handgrip contractions. Older men exhibited improved motor performance after the stressor and concomitant reduction in functional connectivity between the frontal-motor brain regions ipsilateral to the contracting hand. Additionally, while both sexes exhibited significant causal information flow, i.e., effective connectivity, from the frontal to the motor regions of the brain, irrespective of the stressor, older women exhibited a bidirectional effective connectivity between the frontal-motor brain regions after the stressor. Stress had a facilitative effect on the motor performance of older men through compensatory brain network reorganization. Older women exhibited comparable motor performance pre/post stress, despite showing an increase in bidirectional information flow between the frontal-motor areas. Employing brain hemodynamics can facilitate better understanding of the impact of stress on neuromuscular performance and its differential impacts on brain network reorganization between the sexes.

Health-related consequences of the type and utilization rates of electronic devices by college students.

BACKGROUND: College students are leading an evolution of device use both in the type of device and the frequency of use. They have transitioned from desktop stations to laptops, tablets, and especially smartphones and use them throughout the day and into the night. METHODS: Using a 35-min online survey, we sought to understand how technology daily usage patterns, device types, and postures affect pain and discomfort to understand how knowledge of that pain might help students avoid it. Data were analyzed from 515 students (69.5% male) who completed an internet-delivered survey (81.3% response rate). RESULTS: Participants ranked smartphones as their most frequently used technology (64.0%), followed by laptops and tablets (both 53.2%), and desktop computers (46.4%). Time spent using smartphones averaged over 4.4 h per day. When using their devices, students were more likely to adopt non-traditional workplace postures as they used these devices primarily on the couch or at a chair with no desk. CONCLUSION: Recent trends in wireless academic access points along with the portability of small handheld devices, have made smartphones the most common link to educational materials despite having the least favorable control and display scenario from an ergonomic perspective. Further, the potential impact of transitions in work environments due to COVID-19 may further exacerbate ergonomic issues among millions highlighting the need for such work to be carried out.

Anodal tDCS augments and preserves working memory beyond time-on-task deficits.

Transcranial direct current stimulation (tDCS) of the left dorsolateral prefrontal cortex (DLPFC) has been shown to promote working memory (WM), however, its efficacy against time-on-task-related performance decline and associated cognitive fatigue remains uncertain. This study examined the impact of anodal tDCS of the left DLPFC on performance during a fatiguing visuospatial WM test. We adopted a repeated measures design, where 32 healthy adults (16 female), underwent anodal, control and sham tDCS on separate days. They completed an hour long two-back test, with stimulation intensity, onset, and duration set at 1 mA, at the 20th minute for 10 minutes respectively. Task performance, subjective responses, and heart rate variability (HRV) were captured during the experiment. Anodal tDCS substantially improved WM relative to sham tDCS and control in both sexes. These benefits lasted beyond the stimulation interval, and were unique across performance measures. However, no perceptual changes in subjective effort or fatigue levels were noted between conditions, although participants reported greater discomfort during stimulation. While mood and sleepiness changed with time-on-task, reflecting fatigue, these were largely similar across conditions. HRV increased under anodal tDCS and control, and plateaued under sham tDCS. We found that short duration anodal tDCS at 1 mA was an effective countermeasure to time-on-task deficits during a visuospatial two-back task, with enhancement and preservation of WM capacity. However, these improvements were not available at a perceptual level. Therefore, wider investigations are necessary to determine "how" such solutions will be operationalized in the field, especially within human-centered systems.

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics.

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees' safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm's performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

Neural and biomechanical tradeoffs associated with human-exoskeleton interactions.

Industrial passive low-back exoskeletons have gained recent attention as ergonomic interventions to manual handling tasks. This research utilized a two-armed experimental approach (single vs dual-task paradigms) to quantify neural and biomechanical tradeoffs associated with short-term human-exoskeleton interaction (HEI) during asymmetrical lifting in twelve healthy adults balanced by gender. A dynamic, electromyography-assisted spine model was employed that indicated statistical, but marginal, biomechanical benefits of the tested exoskeleton, which diminished with the introduction of the cognitive dual-task. Using Near Infrared Spectroscopy (fNIRS)-based brain connectivity analyses, we found that the tested exoskeleton imposed greater neurocognitive and motor adaptation efforts by engaging action monitoring and error processing brain networks. Collectively, these findings indicate that a wearer's biomechanical response to increased cognitive demands in the workplace may offset the mechanical advantages of exoskeletons. We also demonstrate the utility of ambulatory fNIRS to capture the neural cost of HEI without the need for elaborate dual-task manipulations.

Relationship Between Acute Physical Fatigue and Cognitive Function During Orthostatic Challenge in Men and Women: A Neuroergonomics Investigation.

BACKGROUND: Postflight orthostatic challenge (OC), resulting from blood pooling in lower extremities, is a major health concern among astronauts that fly long-duration missions. Additionally, astronauts undergo physical demanding tasks resulting in acute fatigue, which can affect performance. However, the effects of concurrent OC and acute physical fatigue on performance have not been adequately investigated. OBJECTIVE: The purpose of this study was to determine the relationship between acute physical fatigue and cognitive function during OC. METHODS: Sixteen healthy participants performed the mental arithmetic task and psychomotor tracking tasks in the absence and presence of a prior 1-hour physically fatiguing exercise, on separate days under OC (induced via lower body negative pressure). We recorded task performances on the cognitive tests and prefrontal cortex oxygenation using functional near-infrared spectroscopy, along with physiological and subjective responses. RESULTS: The introduction of the cognitive tasks during OC increased cerebral oxygenation; however, oxygenation decreased significantly with the cognitive tasks under the acute fatigue conditions, particularly during the tracking task and in males. These differences were accompanied by comparable task performances. DISCUSSION: The findings suggest that mental arithmetic is a more effective countermeasure than psychomotor tracking under acute physical fatigue during OC. Whereas females did not show a significant difference in cerebral oxygenation due to task, males did, suggesting that it may be important to consider gender differences when developing countermeasures against OC.

Revealing Sex Differences During Upper and Lower Extremity Neuromuscular Fatigue in Older Adults Through a Neuroergonomics Approach.

Background: Sex differences in neuromuscular fatigue is well-documented, however the underlying mechanisms remain understudied, particularly for the aging population. Objective: This study investigated sex differences in fatigability of the upper and lower extremity of older adults using a neuroergonomics approach. Methods: Thirty community-dwelling older adults (65 years or older; 15 M, 15 F) performed intermittent submaximal fatiguing handgrip and knee extension exercises until voluntary exhaustion on separate days. Muscle activity from prime muscles of the hand/arm and knee extensors were monitored using electromyography, neural activity from the frontal, motor, and sensory areas were monitored using functional near infrared spectroscopy, and force output were obtained. Results: While older males were stronger than females across both muscle groups, they exhibited longer endurance times and greater strength loss during knee extension exercises. These lower extremity findings were associated with greater force complexity over time and concomitant increase in left motor and right sensory motor regions. While fatigability during handgrip exercises was comparable across sexes, older females exhibited concurrent increases in the activation of the ipsilateral motor regions over time. Discussion: We identified differences in the underlying central neural strategies adopted by males and females in maintaining downstream motor outputs during handgrip fatigue that were not evident with traditional ergonomics measures. Additionally, enhanced neural activation in males during knee exercises that accompanied longer time to exhaustion point to potential rehabilitation/exercise strategies to improve neuromotor outcomes in more fatigable older adults.

Neural Correlates of Trust in Automation: Considerations and Generalizability Between Technology Domains.

Investigations into physiological or neurological correlates of trust has increased in popularity due to the need for a continuous measure of trust, including for trust-sensitive or adaptive systems, measurements of trustworthiness or pain points of technology, or for human-in-the-loop cyber intrusion detection. Understanding the limitations and generalizability of the physiological responses between technology domains is important as the usefulness and relevance of results is impacted by fundamental characteristics of the technology domains, corresponding use cases, and socially acceptable behaviors of the technologies. While investigations into the neural correlates of trust in automation has grown in popularity, there is limited understanding of the neural correlates of trust, where the vast majority of current investigations are in cyber or decision aid technologies. Thus, the relevance of these correlates as a deployable measure for other domains and the robustness of the measures to varying use cases is unknown. As such, this manuscript discusses the current-state-of-knowledge in trust perceptions, factors that influence trust, and corresponding neural correlates of trust as generalizable between domains.

A Methodological Framework to Capture Neuromuscular Fatigue Mechanisms Under Stress.

Neuromuscular fatigue is exacerbated under stress and is characterized by shorter endurance time, greater perceived effort, lower force steadiness, and higher electromyographic activity. However, the underlying mechanisms of fatigue under stress are not well-understood. This review investigated existing methods of identifying central mechanisms of neuromuscular fatigue and the potential mechanisms of the influence of stress on neuromuscular fatigue. We found that the influence of stress on the activity of the prefrontal cortex, which are also involved in exercise regulation, may contribute to exacerbated fatigue under stress. We also found that the traditional methods involve the synchronized use of transcranial magnetic stimulation, peripheral nerve stimulation, and electromyography to identify the contribution of supraspinal fatigue, through measures such as voluntary activation, motor evoked potential, and silent period. However, these popular techniques are unable to provide information about neural alterations upstream of the descending drive that may contribute to supraspinal fatigue development. To address this gap, we propose that functional brain imaging techniques, which provide insights on activation and information flow between brain regions, need to be combined with the traditional measures of measuring central fatigue to fully understand the mechanisms behind the influence of stress on fatigue.