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Mental work load or cognitive work load analysis is very important in analyzing the performance of a person. It is one of the most important construct in cognitive ergonomics for correct understanding the performance of a person. One other way to describe the mental work is that it is obtained by collective functioning of all the factors that contribute for the work load efficiency for a given task load. Workload can be described as a mental construct which reflects the mental strain resulting from performing a task under specific environmental and operational conditions which are coupled with the capability of the operator to respond to those demands. Workload is not only task specific, but it is also person specific. Work load not only involves individual capacities but also motivation to perform a task. Task can be said as a demand placed upon the humans. There are many ways to measure the mental work load; some of them are as follows:

1 1) Galvanic skin response or electromodal activity

2 2) Heart rate

3 3) Electroencephalogram

4 4) Eye tracking

Many accidents which occur in the production or manufacturing sector industries are mainly due to the human related factors rather than the machines. The research on the mental work load is started well before 21st century as Sweller et al. [1] in his paper described about the concept of how skill acquisition is related to the mental workload. Similar to their work, Borghini et al. [2] pursued the work of Sweller et al. [1] further and said that making skills will help in reducing the task load. From the work of these two, we can say that the mental work load depends not only on the complexity of the task but also on the skill level of the person who is doing the job. Wang et al. [3] worked on how the mental work load of a person is related to the accidents caused by performing experiments on people solving n-back tasks. Many machine learning deep learning and other generative types of algorithms are used to predict the mental work load like support vector machines [4], hidden Markov model [5], and artificial neural networks [6]. Apart from the behavioral measures like the skill set, many tried to relate the physiological measure like eye pupil diameter, fixation, and gaze [7, 8]. The results of the study have shown that how well the mental work load data can be predicted by neural networks and Bernoulli Boltzmann machines using the eye tracking data and also how well neural networks perform in these types of tasks.