Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

Ding, Yi; Cao, Yaqin; Duffy, Vincent G.; Wang, Yi; Zhang, Xuefeng

doi:10.1080/00140139.2020.1759699

Cited by 62 publications

(38 citation statements)

References 56 publications

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“…It also considers the possibility of each class and the probability of the data belonging to each class. There are many classifiers based on DA, being the most common for the detection of stress in EDA: linear discriminant analysis (LDA) [20,53]; quadratic discriminant analysis (QDA) [13,17,44,68,71] and Gaussian discriminant analysis (GDA) [65].…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

“…A total of 20 papers have only used EDA signals for stress detection [17][18][19][20]24,[27][28][29][30][31][32][33]36,42,43,[45][46][47]49,53,73,103]. The authors have focused on developing methods or evaluating ML models based in EDA and its features.…”

Section: Bio-markers Used In the Papersmentioning

confidence: 99%

“…The use of deconvolution methods were emphasised to obtain the different features of the EDA signals. Another variable that helps to determine different emotional states in the participants is BVP, which combined with EDA gives very good results in the prediction as well [12,15,20,22,38,48,[56][57][58]. Table 5 shows additional variables.The following major cluster (besides EDA and BVP) includes TMP.…”

Section: Bio-markers Used In the Papersmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Sánchez-Reolid¹,

Mt²,

Fernández-Caballero³

2020

Preprint

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Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

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Section: Supervised Learning Methodsmentioning

confidence: 99%

Section: Bio-markers Used In the Papersmentioning

confidence: 99%

Section: Bio-markers Used In the Papersmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Sánchez-Reolid¹,

Mt²,

Fernández-Caballero³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Physical responses, such as muscle fatigue build-up [12] and bad posture [27], can lead to increased difficulty performing manufacturing tasks [28,29]. Mental responses, such as a high cognitive workload, can result in higher stress levels and lower satisfaction, ultimately reducing productivity [12,30]. Therefore, accurate assessment of human physical workload, cognitive workload, and the psychological reactions (e.g., emotions) in humanmachine collaborations is are essential for improving human performance.…”

Section: Human-machine Understanding: Empathy Skillsmentioning

confidence: 99%

Man is not a machine – anthropocentric human-machine symbiosis for ultra-flexible smart manufacturing

Chand¹,

Lu²,

Wang³

et al. 2021

Preprint

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<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

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“…However, mental arithmetic task is an appropriate stress inducing technique but it has certain limitation and according to age it has shown different impact [ 2 ]. To capture the response from different types of subjects in response to various stress induced techniques, multimodality approach has exhibited significant role and its impact is noteworthy [ 3 ]. Various types of studies have been carried out in this domain and quite interesting study has been carried out on the arithmetic task where experimentation has been done on numbers with respect to ordered and unordered [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning

Salankar

Koundal

Qaisar

2021

Journal of Healthcare Engineering

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In this pandemic situation, importance and awareness about mental health are getting more attention. Stress recognition from multimodal sensor based physiological signals such as electroencephalogram (EEG) and electrocardiography (ECG) signals is a very cost-effective way due to its noninvasive nature. A dataset, recorded during the mental arithmetic task, consisting of EEG + ECG signals of 36 participants is used. It contains two categories of performance, namely, “Good” (nonstressed) and “Bad” (stressed) (Gupta et al. 2018 and Eraldeír et al. 2018). This paper presents an effective approach for the recognition of stress marker at frontal, temporal, central, and occipital lobes. It processes the multimodality physiological signals. The variational mode decomposition (VMD) strategy is used for data preprocessing and for the decomposition of signals into various oscillatory mode functions. Poincare plots (PP) are derived from the first eight variational modes and features from these plots have been extracted such as mean, area, and central tendency measure of the elliptical region. The statistical significance of the extracted features with p < 0.5 has been performed using the Wilcoxson test. The multilayer perceptron (MPLN) and Support Vector Machine (SVM) algorithms are used for the classification of stress and nonstress categories. MLPN has achieved the maximum accuracies of 100% for frontal and temporal lobes. The suggested method can be incorporated in noninvasive EEG signal processing based automated stress identification systems.

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Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

Cited by 62 publications

References 56 publications

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Man is not a machine – anthropocentric human-machine symbiosis for ultra-flexible smart manufacturing

Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning

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