Assessment of Mental Fatigue During Examination Period with P300 Oddball Paradigm

Zeba, Mirta Zelenika; Friganović, Krešimir; Palmović, Marijan; Išgum, Velimir; Cifrek, Mario

doi:10.23919/mipro.2019.8756850

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Mental fatigue level was measured for all volunteers, using the score obtained in the self-answered FAS questionnaire. The FAS is a tool which represents a valuable instrument for fatigue assessment with consistent reliability and validity [66], and has been validated in several studies in the literature [67][68][69]. In [67], the use of the FAS was used for the identification of mental fatigue in young adults.…”

Section: Fatigue Assessment Scalementioning

confidence: 99%

“…The FAS is a tool which represents a valuable instrument for fatigue assessment with consistent reliability and validity [66], and has been validated in several studies in the literature [67][68][69]. In [67], the use of the FAS was used for the identification of mental fatigue in young adults. Furthermore, the questions from the FAS, as stated in [69], were selected from four previous valid questionnaires: the fatigue scale (FS), the checklist individual strength (CIS), the emotional exhaustion subscale of the Dutch version of the Maslach Burnout Inventory (MBI-DV), and the Energy and Fatigue Subscale of the World Health Organization Quality of Life assessment (WHOQOL) instrument.…”

Section: Fatigue Assessment Scalementioning

confidence: 99%

“…In the case of this type of model, the RMSE (Equation ( 3)) provides a notion of the performance of the model, and in Figure 7, it is observed that the most accurate model presents very small RMSE values. It is important to address that other studies in this field have reported mental fatigue, drowsiness, or mental workload predictive models, using data from a similar (and even smaller) sample size: 6 [60], 8 [6,26], 10 [22,23,40,87], 11 [42], 12 [5], 13 [24], 14 [67], and 16 [20], to mention a few. Considering the sample sizes of similar studies reported in the identification of mental fatigue, it can be discussed that the proposed pilot study holds its validity.…”

Section: Limitationsmentioning

confidence: 99%

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Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study

Ramírez-Moreno

Carrillo-Tijerina

Candela-Leal

et al. 2021

IJERPH

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Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, β/θ (C3), and the α/θ (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.

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Section: Fatigue Assessment Scalementioning

confidence: 99%

Section: Fatigue Assessment Scalementioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study

Ramírez-Moreno

Carrillo-Tijerina

Candela-Leal

et al. 2021

IJERPH

View full text Add to dashboard Cite

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Mental health and wellbeing of undergraduate students in engineering: A systematic literature review

Asghar,

Minichiello,

Ahmed

2023

J of Engineering Edu

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BackgroundThe wellbeing of college students today is at risk because of rising occurrences of mental health issues in higher education. Concurrently, undergraduate students perceive engineering courses and programs to be among the most arduous, and least welcoming and accommodating, in higher education. Although research related to mental health and wellbeing (MHW) in engineering is growing, a systematic review of this research has yet to be conducted.PurposeThis systematic literature review identifies and synthesizes empirical scholarship related to the MHW of undergraduate engineering students.Scope/MethodSpecified search terms and inclusion criteria were used to identify 34 empirical studies related to engineering undergraduates' MHW. Content and qualitative thematic analyses were conducted to characterize and synthesize trends in research quality and outcomes across studies.ResultsUndergraduate engineering students experience a variety of mental health issues that negatively affect their experiences in engineering education. Stress is the most prevalent mental health issue identified; anxiety, depression, and post‐traumatic stress disorder (PTSD) are also reported. Heavy academic workloads, sleep issues, and the nature of engineering education culture are identified as impediments to MHW in engineering education.ConclusionsAlthough MHW in engineering is a growing area of research internationally, current MHW research in engineering is nascent and focused on the characterization of student mental health issues. Researchers underutilize qualitative and mixed‐methods approaches, longitudinal and experimental designs, guiding frameworks, and robust sampling techniques. Academic and mindfulness interventions, as well as the use of mental wellness constructs from positive psychology, show promise for supporting MHW in engineering.

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Fatigue Analysis in Assembly Line Workers Using Brain Computer Interface

Ahmad,

Liu,

Beyette

2024

SoutheastCon 2024

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Assessment of Mental Fatigue During Examination Period with P300 Oddball Paradigm

Cited by 4 publications

References 24 publications

Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study

Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study

Mental health and wellbeing of undergraduate students in engineering: A systematic literature review

Fatigue Analysis in Assembly Line Workers Using Brain Computer Interface

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