Mental Fatigue Monitoring Using a Wearable Transparent Eye Detection System

Lateral prefrontal cortex thickness is associated with stress but not cognitive fatigue in exhaustion disorder

Introduction

Impaired executive functioning, including cognitive fatigue, is a core feature of the long-term stress-related condition exhaustion disorder (ED). Recent research suggests that a key area for executive control, the lateral prefrontal cortex (LPFC), may be mechanistically linked to cognitive fatigue due to stress. Here, we therefore asked if and how stress, the LPFC and cognitive fatigue may be related in ED.

Methods

We used a multimodal cross-sectional study design with high-resolution structural magnetic resonance imaging (MRI), self-reported measures, and path analysis modeling in 300 participants with ED.

Results

We found positive associations between stress and cognitive fatigue, and stress and LPFC thickness,but no association between LPFC thickness and cognitive fatigue. Furthermore, LPFC thickness did not mediate or moderate the association between stress and cognitive fatigue.

Discussion

These findings suggest that LPFC brain morphology is related to perceived stress levels but not cognitive fatigue, expanding previous research on the role of the LPFC in executive functioning. Moreover, the results support the notion that the LPFC may be mechanistically involved in stress-related executive function impairment but prompt further research into if and how this may be related to cognitive symptoms in ED.

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis

Background

Fatigue is a serious challenge when applying a steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) in the real world. Many researchers have used quantitative indices to study the effect of visual stimuli on fatigue. According to a wide range of studies in fatigue analysis, there are contradictions and inconsistencies in the behavior of fatigue indicators.

New method

In this study, for the first time, a systematic review and meta-analysis were performed on fatigue indices and fatigue caused by stimulation paradigm. We queried three scientific search engines for studies published between 2000 and 2022. The inclusion criteria were papers investigating mental and visual fatigue from performing a visual task using electroencephalogram (EEG) signals.

Results

Attractiveness and variation are the most effective ways to reduce BCI fatigue. Therefore, zoom motion, Newton's ring motion, and cue patterns reduce fatigue. While the color of the cue could effectively reduce fatigue, its shape and background had no effect on fatigue. Additionally, the questionnaire and quantitative indicators such as frequency indices, signal-to-noise ratio (SNR), SSVEP amplitude, and multiscale entropy were utilized to assess fatigue. Meta-analysis indicated that when a person is fatigued, the spectrum amplitude of alpha, theta, and α + θ / β increase significantly, while SNR and SSVEP amplitude decrease significantly.

Conclusion

The outcomes of this study can be used to design more optimal stimulation protocols that cause less fatigue. Moreover, the level of fatigue can be quantitatively assessed with indicators without the participant's self-reports.

Understanding mental fatigue and its detection: a comparative analysis of assessments and tools

Mental fatigue has shown to be one of the root causes of decreased productivity and overall cognitive performance, by decreasing an individual's ability to inhibit responses, process information and concentrate. The effects of mental fatigue have led to occupational errors and motorway accidents. Early detection of mental fatigue can prevent the escalation of symptoms that may lead to chronic fatigue syndrome and other disorders. To date, in clinical settings, the assessment of mental fatigue and stress is done through self-reported questionnaires. The validity of these questionnaires is questionable, as they are highly subjective measurement tools and are not immune to response biases. This review examines the wider presence of mental fatigue in the general population and critically compares its various detection techniques (i.e., self-reporting questionnaires, heart rate variability, salivary cortisol levels, electroencephalogram, and saccadic eye movements). The ability of these detection tools to assess inhibition responses (which are sensitive enough to be manifested in a fatigue state) is specifically evaluated for a reliable marker in identifying mentally fatigued individuals. In laboratory settings, antisaccade tasks have been long used to assess inhibitory control and this technique can potentially serve as the most promising assessment tool to objectively detect mental fatigue. However, more studies need to be conducted in the future to validate and correlate this assessment with other existing measures of mental fatigue detection. This review is intended for, but not limited to, mental health professionals, digital health scientists, vision researchers, and behavioral scientists.

A rapid, non-invasive method for fatigue detection based on voice information

Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms "fatigue," "drowsiness," "vigilance," or "alertness" in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (T_sk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.

Mental Load and Fatigue Assessment Instruments: A Systematic Review

Mental load and fatigue are important causes of performance decreases and accidents in different activities. However, a robust systematic review, detailing the instruments used to quantify them, is currently lacking. The purpose of this study was to summarize and classify by derivations the validated instruments used to quantify mental load and fatigue. The most representative electronic databases in the scope of this review, PubMed, WOS, Scopus, SPORTDiscus, and PsycINFO (until September 2020) were searched for studies that included instruments to analyze mental load and fatigue. The quality of the selected studies was scored using a quality assessment checklist. A total of 40 papers were included. Most of the papers used subjective scales (75%) to quantify mental load and fatigue, with a small presence of behavioral (n = 5) and objective techniques (n = 5). Less is known about the analysis of mental load and fatigue using a combination of derivations. Despite the high cost and complexity of objective techniques, research that applies these measures is important for further analysis of brain processes in mental load and fatigue. The design of a battery of tests that include the three types of derivations also seems necessary.

Detecting Soldiers' Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities

INTRODUCTION

Objectively determining soldiers' fatigue levels could help prevent injuries or accidents resulting from inattention or decreased alertness. Eye-tracking technologies, such as optical eye tracking (OET) and electrooculography (EOG), are often used to monitor fatigue. Eyeblinks-especially blink frequency and blink duration-are known as easily observable and valid biomarkers of fatigue. Currently, various eye trackers (i.e., eye-tracking glasses) are available on the market using either OET or EOG technologies. These wearable eye trackers offer several advantages, including unobtrusive functionality, practicality, and low costs. However, several challenges and limitations must be considered when implementing these technologies in the field to monitor fatigue levels. This review investigates the feasibility of eye tracking in the field focusing on the practical applications in military operational environments.

MATERIALS AND METHOD

This paper summarizes the existing literature about eyeblink dynamics and available wearable eye-tracking technologies, exposing challenges and limitations, as well as discussing practical recommendations on how to improve the feasibility of eye tracking in the field.

RESULTS

So far, no eye-tracking glasses can be recommended for use in a demanding work environment. First, eyeblink dynamics are influenced by multiple factors; therefore, environments, situations, and individual behavior must be taken into account. Second, the glasses' placement, sunlight, facial or body movements, vibrations, and sweat can drastically decrease measurement accuracy. The placement of the eye cameras for the OET and the placement of the electrodes for the EOG must be chosen consciously, the sampling rate must be minimal 200 Hz, and software and hardware must be robust to resist any factors influencing eye tracking.

CONCLUSION

Monitoring physiological and psychological readiness of soldiers, as well as other civil professionals that face higher risks when their attention is impaired or reduced, is necessary. However, improvements to eye-tracking devices' hardware, calibration method, sampling rate, and algorithm are needed in order to accurately monitor fatigue levels in the field.

Anti-fatigue Performance in SSVEP-Based Visual Acuity Assessment: A Comparison of Six Stimulus Paradigms

Purpose

The occurrence of mental fatigue when users stare at stimuli is a critical problem in the implementation of steady-state visual evoked potential (SSVEP)-based visual acuity assessment, which may weaken the SSVEP amplitude and signal-to-noise ratio (SNR) and subsequently affect the results of visual acuity assessment. This study aimed to explore the anti-fatigue performance of six stimulus paradigms (reverse vertical sinusoidal gratings, reverse horizontal sinusoidal gratings, reverse vertical square-wave gratings, brief-onset vertical sinusoidal gratings, reversal checkerboards, and oscillating expansion–contraction concentric rings) in SSVEP acuity assessment.

Methods

Based on four indices of α + θ index, pupil diameter, National Aeronautics and Space Administration Task Load Index (NASA-TLX), and amplitude and SNR of SSVEPs, this study quantitatively evaluated mental fatigue in six SSVEP visual attention runs corresponding to six paradigms with 12 subjects.

Results

These indices of mental fatigue showed a good agreement. The results showed that the paradigm of motion expansion–contraction concentric rings had a superior anti-fatigue efficacy than the other five paradigms of conventional onset mode or pattern reversal mode during prolonged SSVEP experiment. The paradigm of brief-onset mode showed the lowest anti-fatigue efficacy, and the other paradigms of pattern reversal SSVEP paradigms showed a similar anti-fatigue efficacy, which was between motion expansion–contraction mode and onset mode.

Conclusion

This study recommended the paradigm of oscillating expansion–contraction concentric rings as the stimulation paradigm in SSVEP visual acuity because of its superior anti-fatigue efficacy.

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

An oculometrics-based biofeedback system to impede fatigue development during computer work: A proof-of-concept study

A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and 10 females) performed a cyclic computer task for 31–35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system following the detection of fatigue (KSS≥5) in the automatic sessions or by the participants in the manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue with a mean accuracy of approx. 70%. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p = 0.01 Cohen’s d_z = 0.89. The results give support to the effectiveness of integrating oculometrics-based biofeedback in timing plan of micro-breaks to impede fatigue development during computer work.

Mental fatigue level detection based on event related and visual evoked potentials features fusion in virtual indoor environment

The purpose of this work is to set up a model that can estimate the mental fatigue of users based on the fusion of relevant features extracted from Positive 300 (P300) and steady state visual evoked potentials (SSVEP) measured by electroencephalogram. To this end, an experimental protocol describes the induction of P300, SSVEP and mental workload (which leads to mental fatigue by varying time-on-task) in different scenarios where environmental artifacts are controlled (obstacles number, obstacles velocities, ambient luminosity). Ten subjects took part in the experiment (with two suffering from cerebral palsy). Their mission is to navigate along a corridor from a starting point A to a goal point B where specific flickering stimuli are introduced to perform the P300 task. On the other hand, SSVEP task is elicited thanks to 10 Hz flickering lights. Correlated features are considered as inputs to fusion block which estimates mental workload. In order to deal with uncertainties and heterogeneity of P300 and SSVEP features, Dempster-Shafer (D-S) evidential reasoning is introduced. As the goal is to assess the reliability for the estimation of mental fatigue levels, D-S is compared to multi layer perception and linear discriminant analysis. The results show that D-S globally outperforms the other classifiers (although its performance significantly decreases between healthy and palsied groups). Finally we discuss the feasibility of such a fusion proposal in real life situation.

Fatigue Assessment by Blink Detected with Attachable Optical Sensors of Dye-Sensitized Photovoltaic Cells

This paper demonstrates fatigue assessment based on eye blinks that are detected by dye-sensitized photovoltaic cells. In particular, the sensors were attached to the temple of eyeglasses and positioned at the lateral side of the eye. They are wearable, did not majorly disturb the user's eyesight, and detected the position of the eyelid or the eye state. The optimal location of the sensor was experimentally investigated by evaluating the detection accuracy of blinks. We conducted fatigue assessment experiments using the developed wearable system, or smart glasses. Several parameters, including the frequency, duration, and velocity of eye blinks, were extracted as fatigue indices. Successful fatigue assessment by the proposed system will be of great benefit for maximizing performance and maintenance of physical/mental health.