## 4 Execution and Results
### Availability  
In order to check the possibility of using eye-tracking and video recording to recognize emotions of programmers, the availability metrics AV_EYE and AV_VIDEO, respectively, were introduced. For eye tracking, the AV_EYE metric was defined as the percentage of time for which the pupil's readings per minute were above the assumed sample quality threshold. Depending on the required accuracy of the measurements, four thresholds were presented, as shown in Table 1. Over most of the time (64.50%), the device recorded more than 29 readings per minute, with the sampling rate of the device at 30 Hz. Only 3.68% of the 1 min periods were without even a single detected fixation point, and 11.28% were with less than 10. The device did not recognize the position of the pupils when the head was tilted too far over the keyboard and also when the head was turned in one direction or the other. However, the collected data was sufficient to generate video clips with fixations and saccades during the solving of the tasks, as shown in Figure 4.  
Clips from the camera that recorded the faces of the participants were analyzed using Noldus FaceReader software. This recognizes emotional states on the basis of the FACS. For each video frame, the tool provides results as intensiveness vectors for the following emotions: joy, anger, fear, disgust, surprise, sadness and a neutral state. In the case of an error, instead of numerical values, the label FIND_FAILED is returned if the face cannot be detected on the frame, and FIT_FAILED is returned when the emotion cannot be recognized. In order to assess the accessibility of video-based emotion recognition of software developers during work, three metrics were proposed:  
* AV_VIDEO--percentage of time for which emotion was recognized.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Quality Threshold** & **Number of Samples Above Threshold** & **AV\_EYE** \\ \% (Readings per Minute) & **(N = 40,352)** & **AV\_EYE** \\ \hline
**80\%** (\(\geq\)24) & 29,309 & 72.63\% \\
**85\%** (\(\geq\)26) & 28,080 & 69.59\% \\
**90\%** (\(\geq\)27) & 27,512 & 68.18\% \\
**95\%** (\(\geq\)29) & 26,026 & 64.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Eye-tracker sample quality threshold and corresponding availability.  
Figure 4: Fixations and saccades during Task 1.  
* FINDF--percentage of time for which a face was not detected.
* FITF--percentage of time for which the face was detected, but no emotion was recognized.  
The results are shown in Table 2. The average availability across the samples exceeded 77%. However, a thorough analysis of the provided data has shown that the algorithm implemented in the Noldus FaceReader software had a major problem with recognizing the emotions of people with glasses. In this case, the availability decreased to just 55%, whereas for the remaining cases, it equaled 85% (Table 3). Other factors that reduced availability were fringes that partially covered the eyes, beards and moustaches (e.g., participant P21). Therefore, to obtain the best accuracy, the recognition of emotions on the basis of facial expressions can only be used for programmers without glasses or facial hair.