Iranian Journal of Ergonomics

Introduction: Driver fatigue is one of the major causes of accidents in roads. It is suggested that driver fatigue and drowsiness accounted for more than 30% of road accidents. Therefore, it is important to use features for real-time detection of driver mental fatigue to minimize transportation fatalities. The purpose of this study was to explore the EEG alpha power variations in sleep deprived drivers on a car driving simulator.

Materials and Methods: The present descriptive-analytical study was achieved on nineteen healthy male car drivers. After taking informed written consent, the subjects were requested to stay awake 18 hrs before the experiments and refrain from caffeinated drinks or any other stimulant as well as cigarette smoking for 12 hrs prior to the experiments. The drivers sleep patterns were studied through sleep diary for one week before the experiment. The participants performed a simulated driving task in a 110 Km monotonous route at the fixed speed of 90 km/hr. The subjective self-assessment of fatigue was performed in every 10 minute interval during the driving using Karolinska Sleepiness Scale (KSS). At the same time, video recordings from the drivers face and their behaviors were achieved in lateral and front views and rated by two trained observers. Continuous EEG and EOG records were taken with 16 channels during driving. After filtering and artifact removal, power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. To analyze the data, descriptive statistics, Pearson and Spearman coefficients and paired-sample T test were employed to describe and compare the variables.

Results: The findings showed a significant increase in KSS scores in the final 10 minutes of driving (p<0.001). Similar results were obtained concerning video rating scores. Meanwhile, there was a significant increase in the absolute alpha power during the final section of driving (p=0.006).

Conclusion: Driver mental fatigue is considered as one of the major implications for road safety. This study suggests that alpha brain wave rhythm can be a good indicator for early prediction of driver fatigue.

Background and Objectives: These days, due to increasing of old people’s population, elderly is a world widely issue. According to World Health Organization (WHO) people older than 60 years old are called elderly. Becoming old leads to decline in physical ability and increase in physical limitations and therefore there is a need for matching the environment with elderly users. The aim of this study is to develop and valid of check lists for screening the environment risk factor and assessing of elderly functional ability for environment and tools designing.  
Methods: Present study is an analytical and descriptive study, which was performed in 20 houses for old people in Lar city. Data collection was done on existing articles and checklists, and also interview and observation with elderly. Existing checklist was evaluated in validity and reliability with high acceptable level.  
Results: Based on findings of this study, height of mirror of wash-stand, height of wash-stand, dimension of yard and doorway was considered in ergonomics risk factors checklist. Items like opening door personally was involved in Functional Ability checklist. Validity score was lower than 0.78. 
Conclusion: According to findings, it’s necessary to consider elderly limitations and ability in designing environment and tools in order to resolve their problems and increase their quality of life.