Digital biomarker of mental fatigue

Tseng, Vincent W.-S.; Valliappan, Nachiappan; Ramachandran, Venky; Choudhury, Tanzeem; Navalpakkam, Vidhya

doi:10.1038/s41746-021-00415-6

Cited by 18 publications

(10 citation statements)

References 23 publications

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“…These indicators include, for example, a stimulus proceeding negativity (EEG evoked potential, Moser et al, 2017 ), pupil dilation (Strauss et al, 2016 ; Scheffel et al, 2021 ), the activity of particular brain regions detected in fMRI (e.g., Dörfel et al, 2014 ; Ellard et al, 2017 ), as well as fNIRS studies (Lu et al, 2019 ; Azhari et al, 2020 ), but also subjective self-reports of effort and fatigue (Wong et al, 2017 ; Visser et al, 2018 ). Interestingly, digital biomarkers of mental fatigue, like smartphone-based gaze detection, are also being developed (Tseng et al, 2021 ). Discussed indicators of effort, along with examples of their use and main results, are presented in Table 1 (the table does not aim to provide a systematic review of research on ER effort indicators, rather it describes the most popular and promising indicators and provides examples of their use in previous research on fatigue and effort).…”

Section: Self-regulation Self-control and Effortmentioning

confidence: 99%

Emotion Regulation, Effort and Fatigue: Complex Issues Worth Investigating

et al. 2022

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Section: Self-regulation Self-control and Effortmentioning

confidence: 99%

Emotion Regulation, Effort and Fatigue: Complex Issues Worth Investigating

et al. 2022

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“…The questions and possible answers from the FAS are presented in Figure S1 (see Supplemental Material). According to [71], the FAS total score can be classified into three classes: no fatigue (1-21), substantial fatigue (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and extreme fatigue (36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). From the 17 participants, 3 were in the no fatigue class (19 ± 2), 8 in substantial fatigue (27 ± 3), and 6 (37 ± 3) in extreme fatigue.…”

Section: Fatigue Assessment Scalementioning

confidence: 99%

“…Several reports exist in the scientific literature about mental fatigue detection by tracking changes in neural activity through electroencephalography (EEG), which is considered the 'gold standard' for mental fatigue assessment [20]. However, most of these studies present common methodological patterns, including the following: (1) the use of gel-based EEG caps [21][22][23], which require a considerable setup time, proportional to the number of electrodes in the cap; (2) the use of often long (>50 min) fatigue-inducing tests to identify different levels of mental fatigue during model evaluation [20,[23][24][25]; and (3) the construction of predictive models which are highly subject-specific [22,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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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“…Depression and anxiety are the main disorders in the mental health domain, which are broadly experienced by 548 million people worldwide who hardly access effective treatment [127], [128]. mHealth Sensing gives ubiquitous and flexible solutions on both sides of personal mental health management [129]- [132] and population mental health surveying and understanding [133], [134].…”

Section: A Depression and Anxietymentioning

confidence: 99%

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Wang¹,

Xiong²,

Zhang³

et al. 2021

Preprint

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Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either (a) personalized medicine for individuals or (b) public health for populations, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms – (i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloudbased data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: (1) Sensing Task Creation & Participation, (2) Health Surveillance & Data Collection, and (3) Data Analysis & Knowledge Discovery. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.

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Digital biomarker of mental fatigue

Cited by 18 publications

References 23 publications

Emotion Regulation, Effort and Fatigue: Complex Issues Worth Investigating

Emotion Regulation, Effort and Fatigue: Complex Issues Worth Investigating

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

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

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