## 3 Proposed Approach  
The analysis of scientific literature highlights that the problem of fatigue can be focused only if the sleepiness of the subject is the key to measure fatigue, and a way to measure this factor is the evaluation of the PERCLOS. Several ways to measure this parameter are present in the scientific and industrial field [101; 102; 103; 104], the reader can easily think to the cars' system [105; 106; 107; 108] for the identification of the sleepiness of the driver now installed on several car's brands that are capable to control in real-time the sleepiness of the drivers as reported in 2015 by [108]. The industrial application can represent a research frontier since the worker is widely variable in anthropometric and psychological conditions, so devices able to point a specific point in the workplace, as it is done in the cars with sleepiness sensors, is not possible since the worker moves in the workplace. PERCLOS method, instead, fixes a method that is useful to go over these specific aspects that represent a limitation of the fatigue measure. It is worth to note that nowadays some algorithms able to recognize the sleepiness of a human are available and they were developed in an open and free source using a C++ code embedded in MATLAB(r) based on the PERCLOS method. The measurement used in the case of the sleepiness detector for the drivers uses the same principle of the PERCLOS software available on the internet and that is usable thanks to the MATLAB(r), it is possible to get at the following link: [[https://it.mathworks.com/matlabcentral/fileexchange/55152-drowsiness-detection-using-a-binary-svm-classifier](https://it.mathworks.com/matlabcentral/fileexchange/55152-drowsiness-detection-using-a-binary-svm-classifier)]([https://it.mathworks.com/matlabcentral/fileexchange/55152-drowsiness-detection-using-a-binary-svm-classifier](https://it.mathworks.com/matlabcentral/fileexchange/55152-drowsiness-detection-using-a-binary-svm-classifier)). The _PERCLOS_ method is based on the evaluation of the awaking time in a single subject, so the software gives a measure of this assessment calculating the number of frames of a record in which the subject is recognized as awake and comparing this number to the total number of record frames,  
\[PERCLOS=\frac{N_{m}-N_{a}}{N_{m}}\cdot 100 \tag{1}\]where \(N_{m}\) is the total frames number of the record and \(N_{a}\) is the number of the frame in which the subject is recognized as \"awake\", so with the eyes open and without yawing phenomenon. The yawing phenomenon is when a man that is engaged in doing an action repeatedly has an increasing cognitive fatigue and the sleepiness is growing up, so the brain gives the input to increase the oxygen saturation in blood to retard the sleep, so the yawing is a symptom of sleepiness. The problem to use this kind of algorithm is the validation of their use in an industrial context. For this reason, this paper aims to present a first experimental results set using the algorithm for the sleepiness identification through easily available technologies, such as small cameras for eyes pointing and the interface of these cameras. The limitations in use that these experiments revealed and a first curve of fatigue characterization for a single subject during an entire time span of the work shift are reported. The fatigue measurement method using the PERCLOS is very similar to the one used for example in other application fields such as the one of sleepiness definition for the driver of a car, in this case, the measurement system works as described in the following Figure 2.  
As reported in Figure 2, the system acts through a camera that is pointing at the worker's eyes and the image of her/his face is elaborated trying to capture the eyes movements and the yawing of the driver. The recorded videos are then divided into frames in order to allow us the face detection and skin segmentation processes.