Volume 18, Issue 1 (2021)
ioh 2021, 18(1): 249-273 |
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Sadeghian M, Shekarizadeh S, MohammadiFoumani Z. SYSTEMATIC LITERATURE REVIEW ON THE APPLICATION OF DATA MINING METHODS TO MONITOR THE OPERATOR FUNCTIONAL STATE IN HUMAN-MACHINE SYSTEMS. ioh 2021; 18 (1) : 17
URL: http://ioh.iums.ac.ir/article-1-2638-en.html
URL: http://ioh.iums.ac.ir/article-1-2638-en.html
PhD student, Department of Occupational Health and Safety, Student Research Committee, School of Public Health and safety, Shahid Beheshti University of Medical Science, Tehran, Iran , z-mohammadi@sbmu.ac.ir
Abstract: (1830 Views)
Introduction: Continuous monitoring of operator functional state is one of the most important topics and data mining methods are considered as a suitable tool for providing performance evaluation models. However, there has been no comprehensive study on the use of data mining methods in this field so far, and in so doing, the aim of the present article was to systematically review the role and importance of data mining methods to monitor the operator functional state in human-machine systems.
Methods and Materials: A total of 86 published articles evaluated operator functional state were reviewed in five databases. All articles were analyzed in four groups related to operator functional state, three critical safety systems, and three types of data mining techniques. The operator functional state was also assessed through methods of physical measurement, psychophysiological measurement, task-performance indicators, and subjective judgment.
Results: Most of the data mining models were related to the field of road and air transportation, which are mainly focused on fatigue and task-performance indicators. Support vector machine and neural network models were the most frequently used data mining methods. The results showed that most studies were performed on fatigue models, among the functional states where mainly physical measurements were used; however, to psychophysiological measurements were the most frequently measurement method applied to mental workload models and task performance indicators.
Conclusion: A comprehensive evaluation of data mining methods and the parameters used in these models to assess the operator functional state will identify research gaps in this area and conduct more extensive studies to improve human performance.
Methods and Materials: A total of 86 published articles evaluated operator functional state were reviewed in five databases. All articles were analyzed in four groups related to operator functional state, three critical safety systems, and three types of data mining techniques. The operator functional state was also assessed through methods of physical measurement, psychophysiological measurement, task-performance indicators, and subjective judgment.
Results: Most of the data mining models were related to the field of road and air transportation, which are mainly focused on fatigue and task-performance indicators. Support vector machine and neural network models were the most frequently used data mining methods. The results showed that most studies were performed on fatigue models, among the functional states where mainly physical measurements were used; however, to psychophysiological measurements were the most frequently measurement method applied to mental workload models and task performance indicators.
Conclusion: A comprehensive evaluation of data mining methods and the parameters used in these models to assess the operator functional state will identify research gaps in this area and conduct more extensive studies to improve human performance.
Article number: 17
Type of Study: Review Article |
Subject:
Ergonomics
Received: 2019/03/15 | Accepted: 2020/02/21 | Published: 2021/01/2
Received: 2019/03/15 | Accepted: 2020/02/21 | Published: 2021/01/2
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