scholarly journals Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lin Wang ◽  
Hong Wang ◽  
Jintao Liu
Keyword(s):  
Energy Density ◽  
Physiological Signals ◽  
Driver Fatigue ◽  
Distortion Energy

scholarly journals A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

2017 ◽  
Vol 29 (5) ◽  
pp. 479-488 ◽  
Author(s):  
Lin Wang ◽  
Hong Wang ◽  
Xin Jiang
Keyword(s):  
Principal Components ◽  
Physiological Signals ◽  
New Method ◽  
Driver Fatigue ◽  
Independent Variables ◽  
Mode Decomposition ◽  
Calculation Results ◽  
Components Analysis ◽  
Electrocardiogram Ecg ◽  
Contact Sensors

Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG) and electrocardiogram (ECG) collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts) are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD). Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn) of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA) and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.

DETECTION OF DRIVING FATIGUE BY USING NONCONTACT EMG AND ECG SIGNALS MEASUREMENT SYSTEM

2014 ◽  
Vol 24 (03) ◽  
pp. 1450006 ◽  
Author(s):  
RONGRONG FU ◽  
HONG WANG
Keyword(s):  
Detection System ◽  
Biceps Femoris ◽  
Physiological Signals ◽  
Well Performance ◽  
Driver Fatigue ◽  
Training Samples ◽  
Fast Independent Component Analysis ◽  
Kolmogorov Smirnov ◽  
Driving Fatigue ◽  
Fatigue Driving

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

Commercial motor vehicle driver fatigue and alertness study: Technical summary

1996 ◽  
Author(s):  
C.D. Wylie ◽  
T. Shultz ◽  
J.C. Miller ◽  
M.M. Mitler ◽  
R.R. Mackie
Keyword(s):  
Motor Vehicle ◽  
Driver Fatigue ◽  
Commercial Motor Vehicle

Driver fatigue: A study of two types of countermeasures

1976 ◽  
Author(s):  
Stover H. Snook ◽  
James J. Dolliver
Keyword(s):  
Driver Fatigue

Hours of Service and Driver Fatigue: Driver Characteristics Research

2011 ◽  
Author(s):  
Paul P. Jovanis ◽  
◽  
Kun-Feng Wu ◽  
Chen Chen
Keyword(s):  
Driver Fatigue

Interactive Effect of Circadian Rhythm and Time on Task on Driver Fatigue Level

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Zhe Wang ◽  
Hui Zhang ◽  
Qi Zhang ◽  
Siyao Li
Keyword(s):  
Circadian Rhythm ◽  
Interactive Effect ◽  
Time On Task ◽  
Driver Fatigue

Fine-scale taxonomic and temporal variability in the energy density of invertebrate prey of juvenile Chinook salmon Oncorhynchus tshawytscha

2020 ◽  
Vol 655 ◽  
pp. 185-198
Author(s):  
J Weil ◽  
WDP Duguid ◽  
F Juanes
Keyword(s):  
Energy Density ◽  
Chinook Salmon ◽  
Temporal Variability ◽  
Energy Content ◽  
Single Point ◽  
Single Species ◽  
Fine Scale ◽  
Temporal Differences ◽  
Juvenile Chinook Salmon ◽  
Juvenile Chinook

Variation in the energy content of prey can drive the diet choice, growth and ultimate survival of consumers. In Pacific salmon species, obtaining sufficient energy for rapid growth during early marine residence is hypothesized to reduce the risk of size-selective mortality. In order to determine the energetic benefit of feeding choices for individuals, accurate estimates of energy density (ED) across prey groups are required. Frequently, a single species is assumed to be representative of a larger taxonomic group or related species. Further, single-point estimates are often assumed to be representative of a group across seasons, despite temporal variability. To test the validity of these practices, we sampled zooplankton prey of juvenile Chinook salmon to investigate fine-scale taxonomic and temporal differences in ED. Using a recently developed model to estimate the ED of organisms using percent ash-free dry weight, we compared energy content of several groups that are typically grouped together in growth studies. Decapod megalopae were more energy rich than zoeae and showed family-level variability in ED. Amphipods showed significant species-level variability in ED. Temporal differences were observed, but patterns were not consistent among groups. Bioenergetic model simulations showed that growth rate of juvenile Chinook salmon was almost identical when prey ED values were calculated on a fine scale or on a taxon-averaged coarse scale. However, single-species representative calculations of prey ED yielded highly variable output in growth depending on the representative species used. These results suggest that the latter approach may yield significantly biased results.

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