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Showing 2 results for Yadegarfar
Evaluation of Effects of Different Levels of Brightness on Attention and Reaction Time under Laboratory Conditions
Habibollah Dehghan, Zohre Mohebian, Ghasem Yadegarfar,
Volume 4, Issue 4 (Journal of Ergonomics 2017)
Abstract

Introduction: Few studies were conducted to determine the effects of lighting on cognitive performance. However, they could not reach a decisive conclusion. This study investigated cognitive performance of university students exposed to different levels of lighting under laboratory conditions.

Methods: In this experimental study, 33 subjects (age range 19-26 years) performed cognitive tests. Participants were exposed to three levels of lighting (200, 500 and 1500lux) in laboratory conditions while performing CPT tests to investigate percentage of attention and reaction time machine that measures reaction time.

Results: The results of this study showed that the maximum percentage of attention (99.75%) belonged to lighting of 1500 (lux) and the minimum percentage of attention was related to 500(lux) (99.36%). statistical analysis showed significant differences in percentage of attention in different levels of lighting (P=0.004). In addition, results of data analysis showed that increase in intensity of  lighting can make a significant change in the average response time (P˂0.001), correct response (P=0.004), commission error (P=0.001) and omission error (P=0.017). With increasing the lighting intensity, reaction time has decreased. The reaction time showed significant differences  at all levels of lighting (P˂0.001)

Conclusion: According to the findings of this study, lighting causes a decrease in reaction time and increase in attention. Thus, the lighting should be taken into account while designing of job and tasks which need attention or reaction time.


Classification of Physical Work (Load) Based on ANFIS Optimized Model with Genetic Algorithm
Ehsanollah Habibi, Mina Salehi, Ali Taheri, Ghasem Yadegarfar,
Volume 5, Issue 4 (Journal of Ergonomics 2018)
Abstract
Background: Recently adaptive neuro-fuzzy inference system is used for the classification of physical load based on three parameters including %HRmax, HRrest, and body weight. The aim of this study was to optimize this model to reduce the error and increase the accuracy of the model in the classification of physical load.
 Methods: The heart rate and oxygen consumption of 30 healthy men were measured during a step test in the laboratory. The VO2max of the participants was measured directly during a maximal treadmill test. A relationship was observed between the calculated %VO2max which is considered as the gold standard of physical load and the model inputs using ANFIS in MATLAB software version 8.0.0. the genetic algorithm was then applied as an optimization technique to the model.
Results: accuracy, sensitivity, and specificity of the model increased after optimization. The average of accuracy accelerated from 92.95% to 97.92%. The RMSE decreased from 5.4186 to 3.1882. Also, in %VO2max estimation, the maximum error of the mode was ±5% after optimization.
Conclusion: The results of this study show that the use of Genetic Algorithm during training process can increase the accuracy and decrease the error of ANFIS model in the estimation of%VO2max. . The advantages of this model include high precision, simplicity and applicability in real-world working environments and also interpersonal differences.

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