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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.

Investigating the Causes of Nursing Errors and Its Reduction Countermeasures in Recent Studies: A review
Ali Salehi Sahlabadi, Afsaneh Riazat, Sheari Sury, Negar Saffarinia, Zahra Damerchi, Mostafa Pouyakian,
Volume 8, Issue 1 (Iranian Journal of Ergonomics 2020)
Abstract

Background and Objectives: Nursing errors are a serious threat to patient safety, which can lead to increased public concern and distrust of recipients of health care services and refusal to undergo treatment. Therefore, the present study examines types of causes of nurses' errors, reasons for not reporting them and ways to reduce errors.
Methods: The present study was a narrative review study of English and Persian articles on nurses' errors in the period (2008-2018) and was done in 2019. Articles were searched in three internal databases such as SID, Magiran and Iran Medex and five external databases of Google Scholar, PubMed, Scopus, Science Direct, and Springer.
Results: Most nursing errors were in the form of functional errors. Risk factors were divided into nurse, organization, ward, and patient-related error factors. The reasons for nurses' failure to report errors include professional reputation and legal problems. Error reduction strategies are such as nursing education and management controls.
Conclusion: Nursing errors have been studied in various ways. These studies are not only aimed at identifying nurses' errors, but also to enhance knowledge and knowledge about the possible causes and preventive factors. The benefits of this view of the articles lead to the provision of appropriate health care services, proper planning for hospitals by managers, and the advancement of nursing education. However, fewer studies have used modern methods of hazard identification.


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