## 2 Related Work  
So far, several studies, which involved emotion recognition of members of IT teams, have been conducted in the field of software engineering. Numerous attempts were made to identify emotions using various available channels.  
The most comprehensive research on utilizing physiological sensors during software developers' work was conducted by [PERSON] and [PERSON] [5; 9; 10]. During their study [5] on 17 software developers, they collected the following data: electroencephalography (EEG) data using a Neurosky MindBand sensor, temperature, electrodermal activity (EDA) and blood volume pulse (BVP) using an Empatica E3 wrist band, and eye-tracking data using Eye Tribe. The results of the experiment showed that the EDA tonic signal, the temperature, the brainwave frequency bands, and the pupil size were the most useful predictive factors to classify the progress of software developers, and brainwave frequency bands, the pupil size, and heart rate were the most useful to classify their emotions. Nevertheless, they noted strong individual differences with respect to the correlation and classification of physiological data [5]. Similar differences have also been found in our other studies on the use of sensors to monitor the physiology of computer game players [11].  
[PERSON] and [PERSON], along with [PERSON], [PERSON] and [PERSON], also conducted an experiment to classify the difficulty of source code comprehension tasks using the same set of input channels. They stated that it is possible to use off-the-shelf physiological sensors in order to predict software developer task difficulty [12].  
Facial electromyography (fEMG) is commonly regarded as a reliable method for measuring emotional reactions [13]. [PERSON] et al. have proved, in an experiment with 20 participants, that fEMG activities are effective and reliable indicators of negative and positive emotions [14]. [PERSON] et al. successfully used fEMG, along with EDA, to determine emotional responses during an evaluation of mobile applications [15].  
Eye-tracking methods have previously been successfully used in other research in the software engineering domain [16; 17; 18; 19; 20]. For example, [PERSON] and [PERSON] proved the usefulness of eye-movement tracking in a study of the comprehension processes of programmers [21]. An eye-tracking environment (iTrace) has been developed to facilitate eye-tracking studies in software systems [22].  
One of the most popular methods of recognizing emotions is the analysis of facial expressions [23; 24; 25]. It has gained popularity mainly as a universal and non-invasive approach. Algorithms analyze video frames to identify face muscle movements and, on the basis of the Facial Action Coding System (FACS) [26], assess the user's emotional state. Successful attempts are also made to identify emotions on the basis of voice [27]. There are even frameworks that allow such an analysis to be performed [28]. A relatively new approach, which can be well suited for recognizing the emotions of programmers, uses keystroke dynamics and mouse movement analysis. It is completely non-intrusive and does not require any additional hardware [29]. There have already been attempts to use this method to monitor software developers [30].  
The only channel used in previous research (e.g., [31; 32]) that was excluded from the presented study was EEG. The Biometric Stand [33], on which the experiment was conducted, contains only a 3-channel EEG sensor that does not provide reliable data.  
A number of studies have also been conducted on the use of sentiment analysis techniques to identify emotions on the basis of IT project artifacts (e.g., [34; 35]). However, the purpose of this study was to check the ability to recognize emotions of developers while working, and therefore these methods have not been included.