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Repeated and uncontrolled seizures in epilepsy result in brain cell loss and neural inflammation. Current anticonvulsants primarily target ion channels and receptors implicated in seizure activity. Identification of neurotherapeutics that can inhibit epileptiform activity and reduce inflammation in the brain may offer significant benefits in the long-term management of epilepsy. Fenamates are unique because they are both non-steroidal anti-inflammatory drugs (NSAIDs) and highly subunit selective modulators of GABAA receptors. In the current study we have investigated the hypothesis that fenamates have antiseizure properties using mature human stem cell-derived neuro-glia cell cultures, maintained in long-term culture, and previously shown to be sensitive to first, second and third generation antiepileptics. Mefenamic acid, flufenamic acid, meclofenamic acid, niflumic acid, and tolfenamic acid (each tested at 10–100 μM) attenuated 4-aminopyridine (4-AP, 100 μM) evoked epileptiform activity in a dose-dependent fashion. These actions were as effective diazepam (3–30 μM) and up to 200 times more potent than phenobarbital (300–1,000 μM). The low (micromolar) concentrations of fenamates that inhibited 4-AP evoked epileptiform activity correspond to those reported to potentiate GABAA receptor function. In contrast, the fenamates had no effect on neural spike amplitudes, indicating that their antiseizure actions did not result from inhibition of sodium-channels. The antiseizure actions of fenamates were also not replicated by either of the two non-fenamate NSAIDs, ibuprofen (10–100 μM) or indomethacin (10–100 μM), indicating that inhibition of cyclooxygenases is not the mechanism through which fenamates have anticonvulsant properties. This study therefore shows for the first time, using functionally mature human stem cell-derived neuroglial circuits, that fenamate NSAIDs have powerful antiseizure actions independent of, and in addition to their well-established anti-inflammatory properties, suggesting these drugs may provide a novel insight and new approach to the treatment of epilepsy in the future.

Introduction: Complex sociotechnical systems, such as automotive industry, require a proper macro-ergonomic approach to design and implement the work system at micro-ergonomic level. The purpose of this study was to develop and validate effective macroeconomic factors to improve productivity, health and safety of employees in the automotive industry based on Holden & Karsh model. Material and Methods: At the first step, relevant documents were reviewed, and then experts were interviewed to identify macro-ergonomic factors. The identified factors were categorized into four groups based on Holden & Karsh’s model. In the follow-up phase of the study, then, the content validity of the factors was calculated using Delphi technique in the form of an expert panel. Finally, validity acceptance was assessed using mean content validity index, and agreement between experts was evaluated using modified kappa coefficient Results: 28 main factors were identified and categorized into four subgroups, including personnel, unit /department, organization and environment factors. 19 main factors were selected as the effective macro-ergonomic factors to improve employee’s productivity, health, and safety. The content validity index and the modified kappa coefficient were calculated as 0.901 and 0.90, respectively. Conclusion: Macro-ergonomic factors identified in this study can be considered to improve employee’s productivity, health, and safety in the automotive industry. These factors can be used as specific context-based criteria for an ergonomic evaluation in the automotive industry, or even be generalized to other industries, organizations and companies.

Background: Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data. Methods: A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data. Results: According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier. Conclusion: According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

Modeling the complex and prolonged development of the mammalian central nervous system in vitro remains a profound challenge. Most studies of human stem cell derived neurons are conducted over days to weeks and may or may not include glia. Here we have utilized a single human pluripotent stem cell line, TERA2.cl.SP12 to derive both neurons and glial cells and determined their differentiation and functional maturation over 1 year in culture together with their ability to display epileptiform activity in response to pro-convulsant agents and to detect antiseizure drug actions. Our experiments show that these human stem cells differentiate in vitro into mature neurons and glia cells and form inhibitory and excitatory synapses and integrated neural circuits over 6–8 months, paralleling early human neurogenesis in vivo; these neuroglia cultures display complex electrochemical signaling including high frequency trains of action potentials from single neurons, neural network bursts and highly synchronized, rhythmical firing patterns. Neural activity in our 2D neuron–glia circuits is modulated by a variety of voltage-gated and ligand-gated ion channel acting drugs and these actions were consistent in both young and highly mature neuron cultures. We also show for the first time that spontaneous and epileptiform activity is modulated by first, second and third generation antiseizure agents consistent with animal and human studies. Together, our observations strongly support the value of long-term human stem cell-derived neuroglial cultures in disease modeling and neuropsychiatric drug discovery.

Background and Objective: Driver drowsiness is one of the major reasons of severe accidents worldwide. In this study, an electroencephalography (EEG) measurement-based approach has been proposed to detect driver drowsiness. Materials and Methods: The driving tests were conducted in a driving simulator to collect brain data in the alert and drowsy states. Nineteen healthy men participated in these tests. The EEG signals were recorded from the central, parietal, and occipital regions of the brain. 12 features of EEG signal were extracted; then through neighborhood component analysis (NCA), a feature selection method, 6 features including mean, standard deviation (SD), kurtosis, energy, entropy, and power of alpha band in 11-15 Hz, where alpha spindles occur, were selected. For the drowsiness stages assessment, the Observer Rating of Drowsiness (ORD) was applied. Four classifiers including k-nearest neighbor (KNN), support vector machine (SVM), classification tree, and Naive Bayes were employed to classify data. Results: The classification trees detected drowsiness in the early stage with 88.55%. The classification results showed that if only single-channel P4 was used for detecting drowsiness, the better performance could be achieved in comparison to using data of all channels (C3, C4, P3, P4, O1, O2) together. The best performances were 93.13% which were obtained by the classification tree based on data of single-channel P4. Conclusion: This study suggested that the driver drowsiness was detectable based on single-channel P4 in the early stage.

Background and aims: According to the World Health Organization (WHO), unfortunately, about 1.35 million people are killed in traffic accidents annually, and more than 16,000 are killed on Iranian roads, according to the National Forensics Organization. It is estimated that the third leading cause of death in 2020 will be road traffic crashes around the world. This has led traffic accidents and injuries to be considered a globally serious public management problem. Although the restrictions of urban settings appear to reduce accidents, the frequency of these events remains alarming. There are four elements that affect traffic collisions: road, vehicle, environment, and human factor - which plays a more prominent role than other elements. In this context, it is important to examine the behavior of urban taxi drivers, who constitute a significant portion of urban road users. Driving is a complex practice that is influenced by different variables such as age, gender, mentality, and health status. Driving behavior can be predicted by psychological factors such as attachment and personality. Personality have long been recognized as one of the major factors that are closely related to dangerous behaviors and traffic collisions. The results of many studies indicate that personality is associated with car accidents. For example, risky driving behavior is inversely related to friendliness, directly related to emotion-seeking, and inversely related to agreeableness and conscientiousness. High extraversion, low compliance, and low conscientiousness are also associated with neural and neural driving styles. Risky driving is also associated with high neuroticism, low compliance, and low conscientiousness. Therefore, it can be stated that there is a relationship between driving style and personality dimensions. It can be said that driving style is a type of behavior that is subordinate to personality traits and the assumption that personality affects driving has prompted many researchers to study the relationship between personality factors and driving style. The group of people who are most present on urban roads is taxi drivers who form a significant part of the urban driver community. However, few studies have so far been devoted to examining the driving behavior of taxi drivers. Investigating the relationship between personality and driving style of taxi drivers can provide a better understanding of their performance in specific traffic situations. Despite the importance of this issue, it has not been addressed in previous research. Therefore, the purpose of this study was to investigate the relationship between the driving style of taxi drivers in Tehran city and their personality dimensions. Methods: The study population consisted of all taxi drivers working in taxi stations in Tehran. A total of 89 taxi drivers were randomly selected and were asked to complete the questionnaire. They were asked to complete a questionnaire that included three sections: demographic information, personality questionnaire and driving style questionnaire. Demographic information included questions such as age, education, marital status, and driving history per year. The method of completing the data was that a written questionnaire was provided to the driver and he was required to answer all questions carefully. Each questionnaire took about 10 minutes to complete. After data collection and structuring, IBM SPSS Statistics 24 software was used for data analysis. The 44-item Multidimensional Driving Style Inventory (MDSI) was used to explore the driving style of the subjects. The styles were classified into four categories: inattentive, anxious, nervous, and cautious. A six-point Likert scale (‘never’, ‘rarely’, ‘sometimes’, ‘usually’, ‘most often’, and ‘almost always’) was used to answer each item. The NEO Five-Factor Inventory (NEO-FFI) was employed to assess the personality dimensions of individuals. This questionnaire consists of 60 questions that assess the personality of the subjects in terms of extraversion, conscientiousness, neuroticism, openness, and agreeableness. A five-point Likert scale (from ‘strongly agree’ to ‘strongly disagree’) was adopted to respond to each item. Correlation analysis and stepwise regression were used to analyze the results. Using regression modeling, an attempt was made to discover a relationship that would predict driving styles by considering personality dimensions of taxi drivers. To this end, four regression models were used, each considering one driving style as the dependent variable. Besides, the five personality dimensions were regarded as predictor (independent) variables. Possible relationships between these variables were probed using stepwise regression. Results: The average age of drivers who completed the questionnaires was about 44 years, with a standard deviation of around 11 years. This indicates that the age distribution of participants was acceptable, such that all related age groups cooperated in filling out the two inventories. It was found that 6% of drivers had less than 4 years, 12% between 5-10 years, 27% between 11-15 years, 28% between 16-25 years, and 27% more than 26 years of driving experience. These figures illustrate the long driving history of the subjects and their professionalism. For the educational status of the questionnaires, five categories of "less than high school", "between high school and postgraduate", "bachelor", master "and" doctorate "were considered. The results showed that 41% had less than high school education, 40% between high school and postgraduate, 16% had a bachelorchr('39')s degree and 3% had a masterchr('39')s degree. The marital status of the participants showed that 15% of the individuals were single and 85% were married. The mean scores of drivers were moderate in terms of inattentiveness, anxiety, and nervousness, but high with respect to cautiousness. Concerning personality, the drivers scored higher in terms of conscientiousness, extraversion, and agreeableness than openness and neuroticism. Of course, because it is a negative personality traits, the low score of neuroticism is desirable and positive. The results of correlation analysis demonstrated that inattentive driving style is negatively correlated with conscientiousness and agreeableness. By the same token, anxious driving style is negatively correlated with conscientiousness, agreeableness, and extraversion, but positively correlated with neuroticism. Nervous driving style is similar to anxious driving style. However, the results of the cautious driving style are somehow opposite to the nervous and anxious driving styles; thus, this style is positively correlated with conscientiousness, agreeableness, and extraversion. In other words, the personality dimensions of conscientiousness and agreeableness are inversely correlated with unsafe driving styles (inattentive, anxious, and nervous) but directly related to the cautious driving style. This paper also used regression modeling to explore the relationships of variables and attempted to establish a relationship to predict driving style using taxi driverschr('39') personality dimensions. For this purpose, four regression models were considered, in each of which one of the driving styles was considered as dependent variable and five personality dimensions were considered as predictor or independent variables. In all models, conscientiousness entered the regression equations in the first step. The dimensions of agreeableness and openness that came in the second step of the nervous and reckless driving styles, respectively, have a relative impact on driving styles. But since extroversion and neuroticism have not entered into any of the regression equations, it can be concluded that these variables have no effect on the driving style of taxi drivers in the presence of conscientious personality dimension. Therefore, the results of regression equations also indicated that conscientiousness is the main characteristic which predicts one’s driving style. In addition, the negative coefficient of conscientiousness on anxious, nervous, and inattentive driving styles displays its inverse relationship with these styles, while its positive coefficient on cautious driving style suggests its direct relationship with this style. These results also corroborate the findings obtained via correlation analysis of these variables. The same result is also evident in the correlation between agreeableness and driving styles. Since conscientiousness and agreeableness are positive personality traits, their negative association with unsafe driving styles (anxious, nervous, and inattentive) and their positive relationship with the safe driving style (cautious) are reasonable. It can be concluded, therefore, that the scoring of different driving styles among taxi drivers can be predicted only by the personality dimensions of conscientiousness, agreeableness and openness. Since each personality dimension in the Neo-Short Form Questionnaire has 12 questions, while using the NEO Personality Questionnaire we have an estimate of the taxi driverschr('39') driving style with only 36 questions (conscientious, agreement and openness) Driving style scores can be predicted. Conclusion: Using the results of this study, urban transportation policymakers of Tehran could make more appropriate decisions to improve the behavior of taxi drivers. The prevalence of low conscientiousness and agreeableness among taxi drivers, as people who spend a lot of time transporting citizens, is a matter of great concern and needs to be taken seriously. In this context, it is recommended that a research group be set up to examine more precisely the cause of this problem and to organize appropriate courses or consider incentive programs for resolving this issue. Changing this behavior requires constant cultivation. Long-term strategic plans need to be developed for this purpose. Another suggestion is that taxi drivers be screened periodically (e.g. annually) with regard to their personality traits. Since it is possible to predict the driving style of taxi drivers by means of the obtained regression relations and 36 questions, it is proposed to require drivers with unsafe driving styles to participate in tailored educational programs.

Background and aims: Postural Ergonomics Risk Assessment (PERA) is an ergonomic assessment method to detect the major causes of the cyclic work by focusing on the stressful postures analysis in the form of a task analysis. Because of its ability to detect the ergonomic risk sources in work cycles, this study aims to use this method as a systematic ergonomic analysis tool in order to redesign the tasks in an automotive company and examine its further applications. Methods: In the present study, 12 repetitive work cycles consisting of 78 tasks were evaluated in a vehicle assembly. Each task was analyzed separately in terms of posture, force, duration, repetition and vibration using PERA, ManTRA and cube model. Those sub-tasks, in each cycle, which had the highest risk score were identified as the most risky part of the work cycle and considered in the job redesign process. Results: The results show that most of the work cycles are at high-risk level of which shoulders, elbows and head/neck had the most duration of the stressful postures respectively. Also the high-risk tasks in each work cycle were identified and prioritized to be corrected. Conclusion: The results of this study indicate that the common ergonomic risk assessment methods focus on risk level of each body region and therefore the corrective actions would be taken generally, while PERA method is not only capable to assess the body’s postural risks, but also a systematic tool to analyze the complicated tasks and detect the risk sources of a work cycle by breaking it down into its sub-tasks. So it could be more efficient, in job redesign, to figure out some way to modify the work cycle just by focusing on those stressful parts.

ABSTRACT : With all technological advancements in production technology and robotics, a considerable amount of manual tasks are yet performed by workers. In consequence, the occurrence of that musculoskeletal disorders (MSDs) is an expected common result of manual tasks. Fatigue is one of the common causes which leads to MSDs [1] hence one of the strategies for resolving this issue is to let workers have enough rest allowances to recover from the physiological consequences of exertion. This study aims to suggest well-timed rest allowances in the MAPNA factory (Iran, Tehran). For this purpose, we designed an experiment in a workstation to obtain input data (postures and forces) and used collected data to simulate the working condition for all workers with help of 3DSSPP software. We considered maximum voluntary contraction (MVC) of involved muscles. With all this, the critical muscle was determined for all workers based on specific tasks that they performed in their station. With the help of the Rohmert model, the rest time was calculated for the critical muscle of each worker. The results present worker and task-specific optimal work time and subsequent rest time to prevent MSDs. This approach was used because statistical analysis would highlight workers and tasks which are significantly effective on rest times (for both sources p-value was less than 0.05). This approach can be used for any workstation to suggest well-timed rest allowances.

In this paper, the effects of different grasp types, grasp widths, coupling types, and wearing gloves on pinch-insertion force have been studied using the discrete wavelet transform method, which was proposed for extracting features from the force-time curves during the experiment that simulates the snap-fit assembly procedure. This paper uses collected Salmanzadeh and Landau [4] experiments data as an application for proposed methodology. This method allows to use of all information obtained from the experiment and considers the whole force exertion process instead of one point of curves and at the same time reduce the number of variables without losing information. The results obtained by applying multivariate analysis of variance (MANOVA) on the wavelet coefficients are more sensible and more precise than the results of the conventional method based on only one point of the curve. The results show that both wearing glove and grasp type significantly affect the pinch-insertion forces. The second series of results show that the effects of both grasp width and grasp type on pinch force are significant. The third result series show that both pinch-insertion forces are significantly affected by coupling type with/without wearing gloves. Further analysis was performed on the average wavelet coefficients curves that can explain the cause of MANOVA test results. The results of this paper can be used for ergonomic designing of snap-fits to reduce the potential damage of the assembly process.

Human stem cell-derived neurons are increasingly considered powerful models in drug discovery and disease modeling, despite limited characterization of their molecular properties. Here, we have conducted a detailed study of the properties of a commercial human induced Pluripotent Stem Cell (iPSC)-derived neuron line, iCell [GABA] neurons, maintained for up to 3 months in vitro. We confirmed that iCell neurons display neurite outgrowth within 24 h of plating and label for the pan-neuronal marker, βIII tubulin within the first week. Our multi-electrode array (MEA) recordings clearly showed neurons generated spontaneous, spike-like activity within 2 days of plating, which peaked at one week, and rapidly decreased over the second week to remain at low levels up to one month. Extracellularly recorded spikes were reversibly inhibited by tetrodotoxin. Patch-clamp experiments showed that iCell neurons generated spontaneous action potentials and expressed voltage-gated Na and K channels with membrane capacitances, resistances and membrane potentials that are consistent with native neurons. Our single neuron recordings revealed that reduced spiking observed in the MEA after the first week results from development of a dominant inhibitory tone from GABAergic neuron circuit maturation. GABA evoked concentration-dependent currents that were inhibited by the convulsants, bicuculline and picrotoxin, and potentiated by the positive allosteric modulators, diazepam, chlordiazepoxide, phenobarbital, allopregnanolone and mefenamic acid, consistent with native neuronal GABAA receptors. We also show that glycine evoked robust concentration-dependent currents that were inhibited by the neurotoxin, strychnine. Glutamate, AMPA, Kainate and NMDA each evoked concentration-dependent currents in iCell neurons that were blocked by their selective antagonists, consistent with the expression of ionotropic glutamate receptors. The NMDA currents required the presence of the co-agonist glycine and were blocked in a highly voltage-dependent manner by Mg2+ consistent with the properties of native neuronal NMDA receptors. Together, our data suggest that such human iPSC-derived neurons may have significant value in drug discovery and development and may eventually largely replace the need for animal tissues in human biomedical research.

Introduction: Nowadays, Snap-fits have many applications in the automotive industry, especially in assembly lines. They are used instead of the mechanical joints such as bolts, the cabling joints, and the car interior lining joints. Due to the special form of these fasteners, they are usually assembled manually. So, the form of clips’ contact area and the sharpness of the Snap-fits may cause the skin pains on the worker’s fingertips. This fact can cause an ergonomic study of these joints for reducing stress on workers and increasing their efficiency and health. Material and Method: The Finite Element Method (FEM), which is one of the most recognized methods in numeric calculation and computer simulations, is used in order to study the pressures applied to skin due to the pain receptors in the middle layer of skin, and mechanical tensions created in the workers’ thumbs during their work with clips. In order to do this, first a 3D model of a thumb, based on the CT scan data taken from a real thumb sample was created. To identify the different layers of skin, Boolean operations were used. Afterwards, the limit conditions were determined and finally different structures were made to analyze the size, geometric shape and mechanical properties of the clips. Result: In general, the results show that for those clips that their thumb touching surface is only round in edges, the most pressure on the middle layer of skin decreases as the radius of the round edge increases. Whereas such tensions, lightly increase as the radius increases in those clips that have a hybrid design (the combination of flat and curved surfaces). The result of the material analysis for clips shows that using elastic materials has only a limited effect on reducing tensions (only if rubber like materials are used) and for all tough materials, be it plastic or steel, the intensity of pressure tension would increase in the same manner on all the mentioned items. Conclusion: The results suggest that during assembly work there are different factors which affect the tension on the fingers, factors like geometric structure, material and mechanical properties. However, the magnitude of those items’ effects are not the same, and that data can be observed while making an ergonomic design for clips.

Background and aims: Evaluation of physical pressure during different working conditions is one of the important and applicable aspects of human factors engineering at industrial and construction environments. accurate calculation of physical pressure helps to improve working condition and status of workers. On of the challenges that experts faced with is the optimal assignment of human resources to the diverse tasks with respect to physical features of them. Methods: In this study, we chose 9 casual construction works to clarify our subject. Then we measure the pressure on lumbar area for all person during different position. In order to measuring the pressure, we assume average time that each person be in the different position in a single shift. After that we calculate work risks by means of Chaffin and Doisis model. At the next step in order to assign work to workers we use a linear programming model at which the cost of assignments is equal to work risks and the objective function is to minimize total work risk. Finally, by solving the model, best assignment is achieved. Results: Since we choose 5th percentiles to 95th percentiles anthropometric features, we find out that by increasing height and weight, the risk of all works increases too. However, by means of proposed method, we can make a decision that minimizes person’s work risk. Conclusion: Persons that has higher anthropometric features are not suitable for these kind of construction works and their risk increases up to 20% with respect to other ones. Also using mentioned method for decreasing total work risk among people is made benefits.

Objectives: Nowadays, Snap-fits are used in the automotive industry as a proper alternative for mechanical joints, cabling joints, and car interior lining joints. Due to the special form of these joints, which are assembled manually, the contact area between Snap-fits and the worker’s fingertips can be too small. This can cause skin pain on the worker’s fingertips. Therefore, an ergonomic study of these assembly operations can be useful for the automotive industries. This study was thus undertaken to investigate the severity of mechanical stress on fingertips. Methods: In the first stage, the FEM-Method is used to analyse the influence of some effective factors including gender, age, the thickness of the epidermis of a skilled worker, wearing gloves, amount of force, force angle, and snap-fit material parameters during snap-fit assembly. For this purpose, four thumb models, 50% male and 50% female from 20-29 and from 50-59 years old, are used. Results: The mechanical stress is directly associated with gender, age, thickness of epidermis, and the amount of force, and inversely associated with wearing gloves, and force angle. Discussion: The maximum compressive stress and the greater deformation of skin in the male group as compared to the female group is due to the smaller size of women’s thumbs and a less thick outer layer of women’s skin. Moreover, for old people, a higher elastic modulus leads to a greater stiffness of their skin. Finally, the young people’s modulus does not have a significant effect on the maximum compressive stress and total deformation of the skin.

One of the most important elements of successful implementation and management an ergonomics program in an organization is to have suitable goals. In any industry, the goals of ergonomic interventions should be known and understood to determine their benefits. This study aimed to identify and determine the most important goals of ergonomic interventions from the perspective of experts of Iran in the industries using fuzzy Delphi method. In this qualitative study, which is carried out in 2016, the viewpoints of ergonomics experts of Iran were collected during the three-phase fuzzy Delphi through a series of questionnaires. The data was analyzed using content analysis and fuzzy logic. The ergonomics experts have identified eight criteria as the goals of ergonomic interventions which include increasing employee productivity, reducing staff fatigue, promoting physical health, increasing safety, enhancing job satisfaction, improving the quality of products or services, reducing compensation costs and increasing immaterial value and reputation of organizations. The first five criteria with an average of more than 70% agreement have been reached a consensus by the experts. "Increasing productivity", "reducing fatigue" and "improving physical health" are the most important goals of ergonomic interventions considered as ergonomic criteria to evaluate an ergonomics program. The viewpoints of the Iranian ergonomics experts had a lot in common and were really close to the global approach based on International Ergonomics Association definition of ergonomics and human factors.

The objective of this study was to determine the effects of grasp conditions (types of grasp, grasp width, glove and types of coupling) on maximal pushing force (MPF) and required pinch force (RPF) during snap fit assembly. The results indicated that the type of grasp, the type of coupling and wearing gloves have significant (p

This paper proposes a Fuzzy Group Decision Making approach for ranking work stations based on physical pressure. Fuzzy group decision making approach allows experts to evaluate different ergonomic factors using linguistic terms such as very high, high, medium, low, very low, rather than precise numerical values. In this way, there is no need to measure parameters and evaluation can be easily made in a group. According to ergonomics much work contents and situations, accompanied with multiple parameters and uncertainties, fuzzy group decision making is the best way to evaluate such a chameleon of concept. A case study was down to utilize the approach and illustrate its application in ergonomic assessment and ranking the work stations based on work pressure and found that this approach provides flexibility, practicality, efficiency in making decision around ergonomics areas. The normalized defuzzification numbers which are resulted from this method are compared with result of quantitative assessment of Automotive Assembly Work Sheet auto, it’s demonstrated that the proposed method result is 10% less than Automotive Assembly Work Sheet, approximately.