A comprehensive review of approaches to detect fatigue using machine learning techniques

Hooda, Rohit; Sandeep, Joshi, Vedant; Shah, Manan

doi:10.1016/j.cdtm.2021.07.002

Cited by 42 publications

(20 citation statements)

References 62 publications

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“…For instance, biological features extracted with EEG, electro-oculogram, or heart rate, and physical features such as yawning, drowsiness, or slow eye movements [45]. These approaches may be especially useful in the driving and occupational fields to reduce risks and improve workers' health and well-being [46].…”

Section: Discussionmentioning

confidence: 99%

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

Matías‐Guiu

Delgado‐Alonso

Díez‐Cirarda

et al. 2022

JCM

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Fatigue is one of the most disabling symptoms in several neurological disorders and has an important cognitive component. However, the relationship between self-reported cognitive fatigue and objective cognitive assessment results remains elusive. Patients with post-COVID syndrome often report fatigue and cognitive issues several months after the acute infection. We aimed to develop predictive models of fatigue using neuropsychological assessments to evaluate the relationship between cognitive fatigue and objective neuropsychological assessment results. We conducted a cross-sectional study of 113 patients with post-COVID syndrome, assessing them with the Modified Fatigue Impact Scale (MFIS) and a comprehensive neuropsychological battery including standardized and computerized cognitive tests. Several machine learning algorithms were developed to predict MFIS scores (total score and cognitive fatigue score) based on neuropsychological test scores. MFIS showed moderate correlations only with the Stroop Color–Word Interference Test. Classification models obtained modest F1-scores for classification between fatigue and non-fatigued or between 3 or 4 degrees of fatigue severity. Regression models to estimate the MFIS score did not achieve adequate R2 metrics. Our study did not find reliable neuropsychological predictors of cognitive fatigue in the post-COVID syndrome. This has important implications for the interpretation of fatigue and cognitive assessment. Specifically, MFIS cognitive domain could not properly capture actual cognitive fatigue. In addition, our findings suggest different pathophysiological mechanisms of fatigue and cognitive dysfunction in post-COVID syndrome.

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Section: Discussionmentioning

confidence: 99%

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

Matías‐Guiu

Delgado‐Alonso

Díez‐Cirarda

et al. 2022

JCM

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show abstract

“…Mobile recordings can help detect these changes in brain activity and alert coaches and trainers to intervene and provide rest or other strategies to improve performance and prevent injuries, though we should note that research on the use of mobile EEG for fatigue tracking is still in the early stages, with few in situ validations during actual athletic performance. Further work on developing onboard processing algorithms that can accurately classify fatigue levels from complex noise-prone data will be crucial in validating this approach for realistic sports conditions (e.g., Hooda et al, 2022).…”

Section: Current Applications Of Mobile Technologymentioning

confidence: 99%

Probing brain dynamics correlates of motor expertise with mobile recordings.

Moreau,

Kao,

Wang

2023

Sport, Exercise, and Performance Psychology

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Mobile electroencephalography (EEG) and magnetoencephalography (MEG) technology have the potential to revolutionize the study of motor expertise by providing real-time brain activity data in a non-invasive and portable manner. In the context of sports, these recording techniques have already been used in various applications such as mental fatigue monitoring, concussion assessment, and even talent identification. Here, we discuss the potential for mobile technology to facilitate precise characterization of brain dynamics, and outline a number of challenges for the use of portable technology in this context. Specifically, we argue that mobile brain recordings cannot only improve our understanding of motor activities, athletic performance, and athletes' individual differences, but also provide an opportunity for researchers to exploit the richness and uniqueness of sports environments as a tool to better understand the brain. We close with a discussion of the promise of this body of work for future research in sports and exercise neuroscience.

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“…A similar study has been conducted, but the scope is in the work environment and was carried out in Korea [8]. In several fatigue studies that are mostly done in the world of transportation [9]- [14], on screen time in education has not become a research problem that many people do [15]. However, in several previous studies have indeed been implemented on human-computer interaction, the application of brain waves has not become a standard parameter to be used as an aspect of assessing the conditions that occur, although in several studies the brain wave parameters show a more stable and objective value and can be tested by accurate than other biological parameters [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment

et al. 2023

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In this study, a new method was developed to detect student involvement in the online learning process. This method is based on convolutional neural network (CNN) as a classifier with an emphasis on the preprocessing process combined with a new feature in the form of signal magnitude area (SMA) thresholding. In this study, the data used as training data is a public dataset that emphasizes the decomposition of electroencephalography (EEG) signals into individual signal processing. Twenty subjects were taken to be used as test data, with each subject watching online learning lectures in the field of computer science on three different devices, either with a flat screen, a curved screen or a smartphone screen that is smaller than two standard computer monitors. Based on the study's results, it is known that the change in screen size is inversely proportional to the level of student attention, the smaller the screen, the lower the student's attention. For classification results, the model equipped with SMA thresholding outperformed the standard classifier by 8.33% with a test set of 20 people.

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A comprehensive review of approaches to detect fatigue using machine learning techniques

Cited by 42 publications

References 62 publications

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

Probing brain dynamics correlates of motor expertise with mobile recordings.

Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment

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