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Volume 5, Issue 4 (Journal of Ergonomics 2018)
Iran J Ergon 2018, 5(4): 38-48 |
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Habibi E, Salehi M, Taheri A, Yadegarfar G. Classification of Physical Work (Load) Based on ANFIS Optimized Model with Genetic Algorithm. Iran J Ergon 2018; 5 (4) :38-48
URL: http://journal.iehfs.ir/article-1-494-en.html
URL: http://journal.iehfs.ir/article-1-494-en.html
1- Professor, Department of Occupational Health, Health School, Isfahan University of Medical Sciences, Isfahan, Iran
2- MSc, Department of Occupational Health, Health School, Isfahan University of Medical Sciences, Isfahan, Iran ,salehi.ohs@yahoo.com
3- MSc, Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
4- Associate Professor, Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
2- MSc, Department of Occupational Health, Health School, Isfahan University of Medical Sciences, Isfahan, Iran ,
3- MSc, Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
4- Associate Professor, Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract: (12988 Views)
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.
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.
Type of Study: Research |
Subject:
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Received: 2018/04/6 | Accepted: 2018/05/29 | ePublished: 2018/05/29
Received: 2018/04/6 | Accepted: 2018/05/29 | ePublished: 2018/05/29
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
