Factors associated with different levels of daytime sleepiness among Korean construction drivers: a cross-sectional study

Background Commercial vehicle accidents are the leading cause of occupational fatalities and an increased risk of traffic accidents is associated with excessive fatigue, other health problems as well as poor sleep during work. This study explores individual and occupational factors associated with different levels of daytime sleepiness and identifies their association with driving risk among occupational drivers working at construction sites. Methods This cross-sectional and correlational study adopted a self-reported questionnaire of Korean construction drivers (N = 492). The data were collected from October 2018 to February 2019 using a battery of six validated instruments about participants’ sociodemographic, health-related, and occupational characteristics. One-way ANOVA and multinomial logistic regression were conducted using IBM SPSS WIN/VER 25.0, with a two-tailed alpha of .05. Results Based on the Epworth Sleepiness Scale, “moderate” (31.7%) and “severe” (10.2%) daytime sleepiness groups were identified. There were significant differences in break time, driving fatigue, depressive symptom, subjective sleep quality, physical and mental health, and driving risk among the three groups (all p-values < .001). Driving fatigue (Adjusted Odds Ratio [aOR] = 1.08, 1.17), depressive symptoms (aOR = 0.91, 0.98), subjective sleep quality (aOR = 1.18 in moderate only), and driving over the speed limit (aOR = 1.43, 2.25) were significant factors for determining “moderate” and “severe” daytime sleepiness groups, respectively. Conclusion A significant number of construction drivers experience excessive daytime sleepiness; thus it is important to reduce the negative impact of driving fatigue and other factors on daytime sleepiness. Our study findings suggest that occupational health care providers should pay attention to development and implementation of health management interventions to reduce driving fatigue that incorporate the drivers’ physical, mental, and occupational factors. Professional organizations need to establish internal regulations and public policies to promote health and safety among occupational drivers who specifically work at construction sites.

Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

Application of music in relief of driving fatigue based on EEG signals

In order to solve the problem of traffic accidents caused by fatigue driving, the research of EEG signals is particularly important, which can timely and accurately determine the fatigue state and take corresponding measures. Effective fatigue improvement measures are an important research topic in the current scientific field. The purpose of this article is to use EEG signals to analyze fatigue driving and prevent the dangers and injuries caused by fatigue driving. We designed the electroencephalogram (EEG) signal acquisition model to collect the EEG signal of the experimenter, and then removed the noise through the algorithm of Variational Mode Decomposition (VMD) and independent component analysis (ICA). On the basis of in-depth analysis and full understanding, we learned about the EEG signal of the driver at different driving times and different landscape roads, and provided some references for the study of music in relieving driving fatigue. The results of the study show that in the presence of music, the driver can keep the EEG signal active for more than 2 h, while in the absence of music, the driver’s EEG signal is active for about 1.5 h. Under different road conditions, the driver’s EEG signal activity is not consistent. The β wave and (α + θ)/β ratio of the driver in mountainous roads and grassland road landscape environments are highly correlated with driving time, and β wave is negatively correlated with driving time, and (α + θ)/β is positively correlated with driving time. In addition, the accumulation of changes in the two indicators is also strongly correlated with driving time.

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.

EEG-Based Driving Fatigue Alleviation Using Multi-Acupoint Electrical Stimulation Combine Music Conditioning Method

In the present work, we propose the multi-acupoint electrical stimulation (stimulating the Láogóng point (劳宫PC8) and acupuncture points on waist, shoulders, buttocks of the human body) combined with music conditioning (MESCMC) to alleviate driving fatigue. In our study, the complexity of α and β rhythms of EEG of the drivers, the relative power spectrum of θ and β, as well as two relative power spectrum ratio θ/β and (θ+α)/(α+β) are used as fatigue features during driving. The features of the complexity, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional relative power spectrum features, the changes in driving fatigue features were comprehensively analyzed. The results show that the MESCMC method can effectively alleviate the mental fatigue of drivers. Besides, compared with the single-acupoint electrical stimulation[only stimulating the Láogóng point (劳宫PC8)] (SES) method, the MESCMC method is more effective in relieving driving fatigue.The mitigation equipment is low in cost and practical, and the MESCMC method is individualized and improves the universality of driving fatigue detection and relieve, so will be practical to use in actual driving situations in the future.

Post-stroke fatigue: A factor associated with inability to return to work in patients <60 years—A 1-year follow-up

This study investigated the association between post-stroke fatigue and inability to return to work/drive in young patients aged <60 years with first stroke who were employed prior to infarct while controlling for stroke severity, age, extent of disability, cognitive function, and depression. The Fatigue Severity Scale (FSS) was used to evaluate post-stroke fatigue in this 1-year prospective cohort study. Follow-ups were completed at 3, 6, and 12 months post rehabilitation discharge. A total of 112 patients were recruited, 7 were excluded, due to loss to follow-up (n = 6) and being palliative (n = 1), resulting in 105 participants (71% male, average age 49 ±10.63 years). Stroke patients receiving both inpatient and outpatient rehabilitation were consecutively recruited. Persistent fatigue remained associated with inability to return to work when controlling for other factors at 3 months (adjusted OR = 18, 95% CI: 2.9, 110.3, p = 0.002), 6 months (adjusted OR = 29.81, 95% CI: 1.7, 532.8, p = 0.021), and 12 months (adjusted OR = 31.6, 95% CI: 1.8, 545.0, p = 0.018). No association was found between persistent fatigue and return to driving. Fatigue at admission was associated with inability to return to work at 3 months but not return to drive. Persistent fatigue was found to be associated with inability to resume work but not driving. It may be beneficial to routinely screen post-stroke fatigue in rehabilitation and educate stroke survivors and employers on the impacts of post-stroke fatigue on return to work.