Article Preview
TopIntroduction
The study of human mental workload (MWL) is not new; it has been discussed and researched since 1960s (Kum et al., 2007). It is widely used in the study of human factors and ergonomics for industry fields due to both excessive and low level of MWL could decrease work performance (Nachreiner, 1995). Increasing operators' MWL, or overload, is one of the possible causes of information processing disruptions since the amount of information exceeds their processing capacity. In contrast, a low level of MWL can cause boredom and tend to make mistakes (Ryu & Myung, 2005). With the rapid development of science and technology, sophisticated industrial systems have progressed, in which operators often receive massive MWL task, especially for complex operating procedures in nuclear power plants (NPPs) (Hsieh et al., 2015). Although there are many pieces of evidence to show that well-designed information automation can achieve suitable human operator MWL, analyses of various incidents indicate human errors as still a primary cause for more than 70% of accidents in NPPs (Isaac et al., 2002). Thus, enhancing the safety of NPPs based on the level of operators' MWL is an additional significant concern and a permanent research topic.
MWL is also known as “cognitive workload,” considers perceptual and cognitive demands in particular, excluding other factors such as physical workload (Hwang et al., 2008). MWL could be defined as “the amount of mental work or effort necessary to perform a task in a given period of time” (Gao et al., 2013; Proctor & Van Zandt, 2018). It is induced not only due to the cognitive demands of the tasks but also by other factors, such as time demands, stress, fatigue and the number and complexity of assigned tasks (Sheridan & Stassen, 1979; Xie & Salvendy, 2000). Many studies have used subjective rating methods, such as the NASA-Task Load Index (NASA-TLX), subjective workload assessment technique (SWAT), workload profile (WP) method, etc., to evaluate the human MWL. The main advantages of subjective rating methods are that results are easy to implement, inexpensive, easily administered and they are provided directly by the operators. However, the disadvantage of this method is that rating results can be affected by characteristics of respondents and context surrounding (Dyer et al., 1976). Furthermore, the subjective workload cannot be collected in real time. Thus, developing the methods for measuring human MWL objectively with directly measured physiological signals is critical and helpful, especially for the industrial control system.
Currently, physiological measure methods are getting more and more attention due to rapid technology development. In contrast with subjective rating methods, physiological methods measure directly over time and can provide more accurate results due to using specialized equipment (Chuang et al., 2016). The basic principle of these methods is based on the response of the body to external sources of workload. They are collected directly and used as physical indices or to consider their correlation with MWL (De Waard, 1996). Some of the common psychophysiological methods to measure MWL are cardiopulmonary activity, eye-based measures, speech activity, brain activity and galvanic skin response. Eye response indices have been used to reflect the temporal distribution workload levels in HCI control task. However, most of the studies focus on the relationship of eye response indices to MWL without any research suggesting a method of combining them into a quantitative workload measurement value. Therefore, the main objective of this study was to develop an eye responses-based mental workload (E-MWL) evaluation method in the task of searching and processing of information in user interface control. The fuzzy comprehensive evaluation and entropy method were proposed to develop the E-MWL method based on the combination of eye response data.