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.

Introduction: Mental fatigue is defined as body or soul tiredness which can be caused by stress, overwork, excessive use of drugs or physical or mental illnesses. Fatigue is one of the reasons of productivity loss as well as occurring accidents. Therefore, mental fatigue measurement is of great importance. This study was aimed to determine the consistency of mental fatigue measurement by self-reported VAS and the flicker fusion apparatus.

Material and Methods: A descriptive study was conducted on 30 students. After they had accomplished mental tasks (i.e. mathematical problem solving and responding to MMPI questionnaire), mental fatigued was measured by self-reported VAS and flicker fusion apparatus. To analyze the results, paired sample T-test and Spearman correlation test were applied in SPSS software version 11.5 (P<0.05).

Findings: The results of mental fatigue measurements by both methods of self-reported VAS and the flicker fusion apparatus showed significant increase in mental fatigue after finishing each of the mental tasks. But the findings revealed no consistency between the two methods. 

Background: Driver’s fatigue is a major factor contributing to the prevalence of road accidents. A vast number of city dwellers in most countries use public transport bus services to move around the city. Driver’s fatigue causes job burnout and affects the risk of a traffic accident injuring the public. Several methods have been used to date for evaluating mental fatigue however, using questionnaires tends to be a less time-consuming and more accessible technique. The present study was therefore conducted to develop a mental fatigue questionnaire for public transport bus drivers.

Materials and Methods: The study was conducted based on the criteria used for qualitative research. Semi-structured interviews were held with public transport bus drivers using probing questions and data were collected until their saturation so as to enable access to a direct description of mental fatigue by the bus drivers. Data saturation occurred with 30 interviews and sampling was then discontinued. The analysis of the interviews led to the extraction of the themes and an initial list of questionnaire items was then developed. The psychometric properties of the questionnaire were then evaluated through examining the content validity and internal consistency of the items. The content validity of the items was calculated using Lawshe’s table. A minimum CVR of 0.99 and a minimum CVI of 0.75 denoted an acceptable content validity for the items. To determine the internal consistency of the items, 200 bus drivers completed the final version of the questionnaire. The data obtained were then analyzed in SPSS-16 using Cronbach's alpha to measure the reliability of the questionnaire and considering an acceptance level of 0.7.

Results: The interviews conducted at the beginning of the study with 30 drivers led to the emergence of an initial list with 26 items. A total of 9 items with a CVR less than 0.99 were omitted from the list and 17 items with adequate simplicity, clarity and correlation between them and which had a minimum CVI of 0.75 were kept. The questionnaire had a Cronbach's alpha value of 0.87 and was therefore considered a reliable tool.

Conclusion: The questionnaire developed in this study has a good validity and reliability and can therefore be used to assess mental fatigue in public transport bus drivers.

Background and Objectives: Mental fatigue is a condition triggered by prolonged cognitive activity. Mental fatigue causes brain over-activity. This is a condition where the brain cells become exhausted, hampering person productivity, and overall cognitive function. The aim of this study was to assess students’ mental fatigue using brain indices.
Methods: The present descriptive - analytic study has been conducted on 20 students of the Faculty of Health mean age (SD) of 24.40 (3.73) years old in Ahwaz University of Medical Sciences (2019). To assess the performance of the participants, they were asked to study a text with spelling errors and correct those errors. This activity was performed in five stages, each lasting 15 min and EEG was recorded at all stages, and at each stage, the visual analog scale was completed by participants. Data analysis was done by SPSS 24.
Results: The results showed that the activity of alpha, beta, and theta signals in the first 15 minutes was 0.89±0.30, 0.70±0.33, and 1.19±0.36, and the last 15 minutes, 0.63±0.34, 0.55±0.26, and 1.03±0.34 respectively. Reducing the activity of the signals indicated there has been an increase in the amount of mental fatigue in individuals. Also, using visual analog scale, the individuals have acknowledged that they have experienced symptoms of mental fatigue. Finally, there was no significant relationship between students’ EEG and visual analog scale.
Conclusion: The results showed that alpha, beta and theta indices could be suitable indicators for evaluating mental fatigue. Also, mental fatigue can be one of the factors that affect the accuracy and performance of individuals, so that it can reduce their attention and efficiency.