Application of Fully Adaptive Symbolic Representation to Driver Mental Fatigue Detection Based on Body Posture

2021 ◽  
Author(s):  
Shahzeb Ansari ◽  
Haiping Du ◽  
Fazel Naghdy ◽  
David Stirling
Keyword(s):  
Symbolic Representation ◽  
Mental Fatigue ◽  
Body Posture ◽  
Fully Adaptive ◽  
Fatigue Detection

scholarly journals Study on the Preferred Application-Oriented Index for Mental Fatigue Detection

2018 ◽  
Vol 15 (11) ◽  
pp. 2555 ◽  
Author(s):  
Tianhong Duan ◽  
Nong Zhang ◽  
Kaiway Li ◽  
Xuelin Hou ◽  
Jun Pei
Keyword(s):  
Short Term Memory ◽  
Fatigue Testing ◽  
Mental Fatigue ◽  
The Self ◽  
Short Term ◽  
Flicker Fusion Frequency ◽  
Term Memory ◽  
Severe Fatigue ◽  
Speed Perception ◽  
Fatigue Detection

Most of the research on mental fatigue evaluation has mainly concentrated on some indexes that require sophisticated and large instruments that make the detection of mental fatigue cumbersome, time-consuming, and difficult to apply on a large scale. A quick and sensitive mental fatigue detection index is necessary so that mentally fatigued workers can be alerted in time and take corresponding countermeasures. However, to date, no studies have compared the sensitivity of common objective evaluation indexes. To solve these problems, this study recruited 56 human subjects. These subjects were evaluated using six fatigue indexes: the Stanford sleepiness scale, digital span, digital decoding, short-term memory, critical flicker fusion frequency (CFF), and speed perception deviation. The results of the fatigue tests before and after mental fatigue were compared, and a one-way analysis of variance (ANOVA) was performed on the speed perception deviation. The results indicated the significance of this index. Considering individual differences, the relative fatigue index (RFI) was proposed to compare the sensitivity of the indexes. The results showed that when the self-rated fatigue grade changed from non-fatigue to mild fatigue, the ranges of RFI values for digital span, digital decoding, short-term memory, and CFF were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. Correspondingly, when the self-rated fatigue grade changed to severe fatigue, the ranges of RFI values for the above indexes were 0.415–0.577, 0.482–0.669, 0.329–0.396, and 0.114–0.218, respectively. These results suggest that the sensitivity of the digital decoding, digital span, short-term memory, and CFF decreased sequentially when the self-evaluated fatigue grade changed from no fatigue to mild or severe fatigue. The RFI individuality of the speed perception deviation is highly variable and is not suitable as an evaluation index. In mental fatigue testing, digital decoding testing can provide faster, more convenient, and more accurate results.

Proactive mental fatigue detection of traffic control operators using bagged trees and gaze-bin analysis

2019 ◽  
Vol 42 ◽  
pp. 100987 ◽  
Author(s):  
Fan Li ◽  
Chun-Hsien Chen ◽  
Gangyan Xu ◽  
Li Pheng Khoo ◽  
Yisi Liu
Keyword(s):  
Traffic Control ◽  
Mental Fatigue ◽  
Fatigue Detection ◽  
Control Operators

scholarly journals Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 457
Author(s):  
Quan Liu ◽  
Yang Liu ◽  
Kun Chen ◽  
Lei Wang ◽  
Zhilei Li ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Feature Fusion ◽  
Rapid Development ◽  
Mental Fatigue ◽  
Detection Accuracy ◽  
Health State ◽  
Eeg Signals ◽  
Weight Value ◽  
The Common ◽  
Fatigue Detection

With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don’t achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy.

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