Stress; The Vulnerability and Association With Driving Performance

Shamsul, B. M. T.; Khairunnisa, S.; Ng, Yee Guan; Syah, Muziman

doi:10.3844/ajassp.2014.448.454

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Notably, human factors, particularly risky behaviors, emerge as primary contributors to road traffic accidents, especially among long-haul drivers [2]. Complementing these human-related factors are considerations related to vehicles, road conditions, and the surrounding environment, collectively influencing the occurrence of road traffic accidents (RTAs) [3].…”

Section: Introductionmentioning

confidence: 99%

Factors Associated with Occupational Ergonomics and Health Risk Among Public Vehicle Drivers in Kathmandu Valley

Singh,

Neupane,

Singh

2023

Int J Sci Healthcare Res

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Background: Driving is common in Kathmandu, vital for livelihood. Driver safety is crucial. Ergonomics create safe workspaces. Weather, traffic, risky behaviours cause accidents. Ergonomic mods reduce injuries, promote safety for all. Address driver health issues for a healthier transportation system. The prevalence of low back pain (LBP) was high at 73.9%. LBP was significantly associated with work duration exceeding 10 years, working more than 8 hours per day, and having more accidents while driving the bus in the previous year. Among ergonomic risk factors, individuals who reported discomfort with seats and steering wheels had a significantly higher LBP prevalence. Methodology: Descriptive cross-sectional design assessed ergonomic factors and health risk among public vehicle drivers in Kathmandu Valley. Study covered three districts (Kathmandu, Lalitpur, and Bhaktapur) for accessibility and diverse vehicles. Time Duration: Bhadra 2077 to Ashadh 2078. Study. Public drivers aged 20 to below 60 years, with at least two years' experience. This are the primary data from a structured questionnaire whereas the sample size were 394 drivers calculated with 95% confidence interval and 5% marginal error. And convenience and purposive sampling technique was applied the data were collected by Face-to-face interviews using a structured questionnaire on socio-demographics, economics, occupation, environment, workload, and health risk factors. Findings: Within the last year, respondents experienced ergonomics-related health risks: fatigue (27.7%), LBP (22.5%), body ache (10.9%), shoulder pain (7.1%), neck pain (5.3%), MSD (4.8%), and RTA injuries (4.8%). Other health issues observed were respiratory problems (9.6%), gastritis (10.1%), headache (8.1%), blood pressure-related problems (4.8%), blood sugar issues (3.6%), and Covid-19 impact (1.5%). Overall, 4.96% reported health problems, with 0.39% requiring hospitalization during that period. Conclusion: Overall, the study found associations between certain factors (e.g., alcohol, speed, work hours, route duration, seat comfort, height-adjustable seat, foldable seat) and health risks (severe injuries, hospitalization after RTA, sleeping disorder, mental stress, body ache, fatigue, LBP, neck pain, MSD). Keywords: [Ergonomics, Occupational Ergonomics, Public Vehicles, Health Risks.]

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Section: Introductionmentioning

confidence: 99%

Factors Associated with Occupational Ergonomics and Health Risk Among Public Vehicle Drivers in Kathmandu Valley

Singh,

Neupane,

Singh

2023

Int J Sci Healthcare Res

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“…Also, the results have indicated that the alpha wave is significantly affected by the stressed induced and binaural beats sounds. In 2014, a study was done by Shamsul et al [6], where the goal was to compare driving stress, fatigue and driving error between complex and monotonous driving. The authors have reported that the highest mean was observed in the theta waves compared to alpha and beta waves.…”

Section: Introductionmentioning

confidence: 99%

In-Vehicle Stress Monitoring Based on EEG Signal

Begum¹,

Ahmed²

2017

IJERA

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In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers' stress monitoring system based on electroencephalography (EEG) signals enabling individual---focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

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