## 2 Material and Methods
### Experimental Variables  
Experimental Variables can be obtained from the operators' workplace. We developed an employee information extraction point (see Figure 4) that can be implemented in every operators' workplace. Environmental variables are determined by ambient parameters in the data center. One important variable is noise (although temperature IoT sensors could be subsequently added). The microphone \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of the Variable** & **Variable Type** & **Function** & **Measureer Tool** \\ \hline Environmental variable (I) & Boolean & Noise level sensor & Microphone \\ Environmental variable (II) & Integer & Temperature and humidity sensor & Sensor \\ Biometric variable & Boolean & Heart rate measurer & Heartbeat wristwatch \\ Status variable (I) & Integer & Facial Emotion Recognition & Web Cam \\ Status variable (II) & Integer & Emotional Prosody & Microphone \\ Status variable (III) & Integer & Mood, Work, Environment and & App/Voice Assistant \\ System health variable & Integer & Coordination Status & Health Measure Program \\  & & Health of the system & (CPU, memory, logs...) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables from Employee Information Extraction Point. Figure 4: Employee Information Extraction Point. connected to the room will measure the periods of time that exceed a maximum level of noise (determined, in our case according to European Union Directive on Environmental Noise (2002/49/EC), between 40-50 dBA) [24], indicating an exceptional situation of the system, therefore a possible bad influence in this environment. This variable will be Boolean, so it will only take the 0/1 values. We are using biometric sensors to control heart rate and the excitement of the workers. This biometric variable, as well as environmental ones, will be of Boolean type. According to the person approach and [PERSON]'s error model (1990) [25; 26], it is necessary to monitor, not only the worker, but also the workplace. We built three status variables from three types of input sources (included in Table 1), all of them extracted from the employee information extraction point (Figure 4). These status variables are very relevant in our study, as we intend to consider emotions as a key base in complex infrastructures and try to relate them to the infrastructure productivity. The first status variable is a facial emotion recognition variable, which is obtained from a collection of photos through the webcam of the employee information extraction point. We developed a facial recognition algorithm and trained it with our own dataset (Figure 5), formed by a combination of different data sources: photos from Extended Cohn-Kanade [27; 28] and Recola database, and a collection of images of our research group personnel. These last images were taken from a video data source. As a result, we obtained a model able to detect the worker emotion with 79.23% accuracy. For the second status variable, we created a mobile and tablet application, using a voice assistant alternatively, where we ask, every thirty minutes, different questions about workload, mood of the worker, opinions of the system, etc. The idea is not to be intrusive in the workplace applications, and get extra data, that could show us possible unexpected situations that may affect the personnel from the command control center, like bad moods or simply fatigue. These devices are also present in the employee extraction point. The third status variable is obtained from the voice of the workers. We made a model for audio analysis, which will give us the emotional prosody of the operator, expressed in terms of valence and arousal. We built our own audio recognizer, training a CNN with audio from Recola database and from our research group personnel. This last audio was taken from the same video that the images used in facial recognition. The best model obtained was able to predict the arousal with a Concordance Correlation Coefficient (CCC) of 0.8449 and the valence with a 0.2595 CCC.