Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

Zhang, Chong; Zheng, Chen; Yu, Xiaolin

doi:10.1007/s11434-008-0245-1

Cited by 17 publications

(8 citation statements)

References 34 publications

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“…SVM is adopted to train a prediction model (or classifier) based on the labeled training data set, and hence, this model can be used to predict which category the other data set belongs to. As the EEG data are recorded by sampling and then segmented by moving window, such data are discrete and separable (Zhang et al , 2008; Chai et al , 2016). In the classifier training process, 50 per cent of the data are used as the training set and the other 50 per cent act as the testing set.…”

Section: Resultsmentioning

confidence: 99%

Detection and estimation of mental fatigue in manual assembly process of complex products

Hong

Duan

Zhang

et al. 2017

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Purpose This paper aims to investigate an approach for mental fatigue detection and estimation of assembly operators in the manual assembly process of complex products, with the purpose of founding the basis for adaptive transfer and demonstration of assembly process information (API), and eventually making the manual assembly process smarter and more human-friendly. Design/methodology/approach The proposed approach detects and estimates the mental state of assembly operators by electroencephalography (EEG) signal recording and analysis in an engine assembly experiment. When the subjects perform assembly tasks, their EEG signal is recorded by a portable EEG recording system called Emotiv EPOC+ headset. The feature set of the EEG signal is then extracted by calculating its power spectrum density (PSD), followed by data dimension reduction based on principal component analysis (PCA). The dimension-reduced data are classified by using support vector machines (SVMs), and hence, the mental state of assembly operators can be estimated during the assembly process. Findings The experimental result shows that the proposed approach is able to estimate the mental state of assembly operators within an acceptable accuracy range, and the PCA-based dimension reduction method performs very well by representing the high-dimensional EEG feature set with just a few principal components. Originality/value This paper provides theoretical and experimental basis for the API transfer and demonstration based on human cognition. It provides a new idea to seek balance between the improvement of production efficiency and the sustainable utilization of human resources.

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Section: Resultsmentioning

confidence: 99%

Detection and estimation of mental fatigue in manual assembly process of complex products

Hong

Duan

Zhang

et al. 2017

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“…Subjective evaluation relies on questionnaires to obtain each individual's fatigue status [8,9]. The fatigue level is measured subjectively by assessing eyestrain, dif¿culty in focusing, headache and so forth [10,11]. Although subjective assessment is a reliable method for evaluating an individual's fatigue state, it is easily affected by individual differences, and there are non-uniform evaluation criteria [12].…”

Section: Introductionmentioning

confidence: 99%

P-31: Visual Fatigue Assessment and Modeling Based on ECG and EOG Caused by 2D and 3D Displays

Yang

Wang

et al. 2016

SID Symposium Digest of Technical Papers

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“…Subjective sleepiness was assessed using the Stanford Drowsiness Scale (SSS) and the Karolinska Sleepiness Scale (KSS), using the Samn-Perelli Scale (SPC), Subjective Fatigue Rating Scale (SFS), and Borg Rating Scale (CR-10) [10]. The comparison results of several subjective scale scores before and after the experimental task are shown in Figure 2.…”

Section: Simulation Results and Analysis A Subjective Evaluation Of Exercise-induced Fatiguementioning

confidence: 99%

Feature Extraction and Simulation of EEG Signals During Exercise-Induced Fatigue

Yang

Ren

2019

IEEE Access

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Accurate extraction of EEG signal characteristics during exercise fatigue can provide a scientific basis for sports fatigue detection and exercise fatigue injury treatment. In this paper, based on multivariate empirical mode decomposition (MEMD) and Hilbert-Huang (HHT) algorithm, feature extraction of EEG signals during exercise fatigue is performed. MEMD extends standard experience mode to multi-channel signal processing and solves traditional algorithms. It is not suitable for self-adaptability, modal aliasing, and scale alignment. It is suitable for analyzing multi-time sequence; multi-channel and multi-scale EEG signal decomposition. After the original EEG signal passes through the MEMD, the energy mean, median and standard deviation of the EEG bands in different levels are calculated and used to form the feature set. Then the support vector machine (SVM) classifier is used to classify the extract the extracted features. The simulation results show that the proposed method can effectively extract the features of EEG signals during exercise fatigue.INDEX TERMS Exercise fatigue, EEG signal, multivariate empirical mode decomposition, Hilbert-Huang transform.

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Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

Cited by 17 publications

References 34 publications

Detection and estimation of mental fatigue in manual assembly process of complex products

Detection and estimation of mental fatigue in manual assembly process of complex products

P-31: Visual Fatigue Assessment and Modeling Based on ECG and EOG Caused by 2D and 3D Displays

Feature Extraction and Simulation of EEG Signals During Exercise-Induced Fatigue

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