Evaluation of Driver Stress Using Biomarker in Motor-vehicle Driving Simulator

2006 ◽  
Author(s):  
M. Yamaguchi ◽  
J. Wakasugi ◽  
J. Sakakima
Keyword(s):  
Driving Simulator ◽  
Motor Vehicle ◽  
Vehicle Driving Simulator ◽  
Driver Stress ◽  
Motor Vehicle Driving

scholarly journals Evaluation of Driver Stress Using Motor-vehicle Driving Simulator

2006 ◽  
Vol 126 (8) ◽  
pp. 438-444 ◽  
Author(s):  
Mitsuo Deguchi ◽  
Junichi Wakasugi ◽  
Tatsuya Ikegami ◽  
Shinji Nanba ◽  
Masaki Yamaguchi
Keyword(s):  
Driving Simulator ◽  
Motor Vehicle ◽  
Vehicle Driving Simulator ◽  
Driver Stress ◽  
Motor Vehicle Driving

scholarly journals Commercial Motor Vehicle Driving Simulator Validation Study: Phase II

2011 ◽  
Author(s):  
Justin F. Morgan ◽  
Scott A. Tidwell ◽  
Alejandra Medina ◽  
Myra Blanco ◽  
Jeffrey S. Hickman ◽  
...  
Keyword(s):  
Phase Ii ◽  
Validation Study ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Study Phase ◽  
Vehicle Driving Simulator ◽  
Commercial Motor Vehicle ◽  
Motor Vehicle Driving

341 Acute effects of seltorexant, a selective orexin-2 antagonist (JNJ- 42847922), on driving after bedtime administration

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A136-A136
Author(s):  
S Brooks ◽  
R G J A Zuiker ◽  
G E Jacobs ◽  
I Kezic ◽  
A Savitz ◽  
...  
Keyword(s):  
Postural Stability ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Subjective Effects ◽  
Driving Ability ◽  
Post Dose ◽  
Double Blind ◽  
Increased Risk ◽  
Subjective Sleepiness ◽  
The Difference

Abstract Introduction Seltorexant (JNJ-42847922), a potent and selective antagonist of the human orexin-2 receptor, is being developed for the treatment of major depressive disorder. Seltorexant also has sleep-promoting properties. Investigating the effects of sleep-promoting medications on driving is important because some of these agents (e.g. GABAA receptor agonists) may be associated with increased risk of motor vehicle accidents. We evaluated the effect of seltorexant on driving after forced awakening at night, using a validated driving simulator. Methods This double-blind, placebo and active-controlled, randomized, 3-way cross-over study was conducted in 18 male and 18 female healthy subjects. All subjects received seltorexant 40 mg, zolpidem 10 mg, or placebo 15 minutes before bedtime. Eighteen subjects were awakened at 2- and 6-hours post-dose, and the other 18 at 4- and 8-hours post-dose. At those timepoints, pharmacokinetics, objective (standard deviation of the lateral position [SDLP]) and subjective effects (using Perceived Driving Quality and Effort Scales) on driving ability, postural stability and subjective sleepiness were assessed. Results For seltorexant, the SDLP difference from placebo (95% confidence interval) at 2-, 4-, 6- and 8-hours post-dose was 3.9 cm (1.26, 6.60), 0.9 cm (-1.08, 2.92), 1.1 cm (-0.42, 2.63), and 0.6 cm (-2.75, 1.55), respectively vs. 9.6 cm (6.97, 12.38), 6.6 cm (3.53, 9.60), 4.7 cm (1.46, 7.85), and 1.3cm (-1.16, 3.80), respectively for zolpidem. The difference from placebo was significant at 2-hours after taking seltorexant, while the difference from placebo was significant at 2, 4 and 6-hours after zolpidem. Subjective driving quality was decreased for both drugs at all time points and driving effort was increased up to 4-hours post-dose for both medications. Subjective sleepiness showed a significant increase compared to placebo 2- and 4-hours after administration of either drug. Postural stability was decreased up to 2-hours after administration of seltorexant, and up to 4-hours after administration of zolpidem. Conclusion Compared to zolpidem, objective effects on driving performance were more transient after seltorexant administration and largely normalized by 4–6 hours post-dose. Support (if any) This work was sponsored by Janssen R&D.

6-DOF Motion System of Amphibious Vehicle Driving Simulator Motion Parameter Design Research

2011 ◽  
Vol 460-461 ◽  
pp. 704-709
Author(s):  
Shu Tao Zheng ◽  
Zheng Mao Ye ◽  
Jun Jin ◽  
Jun Wei Han
Keyword(s):  
Design Research ◽  
Driving Simulator ◽  
Motion Parameter ◽  
Driving Simulators ◽  
Motion System ◽  
Vehicle Acceleration ◽  
Vehicle Driving Simulator ◽  
Simulator Motion ◽  
Result Analysis ◽  
The Relationship

Vehicle driving simulators are widely employed in training and entertainment utilities because of its safe, economic and efficient. Amphibious vehicle driving simulator was used to simulate amphibious vehicle on land and in water. Because of the motion difference between aircraft and amphibious vehicle, it is necessary to design a reasonable 6-DOF motion system according to the flight simulator motion system standard and vehicle motion parameter. FFT of DSP and PSD were used to analysis the relationship between them. Finally according to the result analysis, a set of reasonable 6-DOF motion system motion parameter was given to realize the driving simulator motion cueing used to reproduce vehicle acceleration.

scholarly journals Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

2020 ◽  
Vol 10 (16) ◽  
pp. 5673 ◽  
Author(s):  
Daniela Cardone ◽  
David Perpetuini ◽  
Chiara Filippini ◽  
Edoardo Spadolini ◽  
Lorenza Mancini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress State ◽  
Thermal Imaging ◽  
Driving Simulator ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Thermal Features ◽  
Driver Stress

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

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